Friends, we forgot to tell you that we have a website- www.dtbc.org.uk . Our latest works, including our community research project examining the use of EdTech and its implications on Black communities, are all hosted there.

This account is now dormant.

New website for our latest work was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.

Data collection is such a nuanced activity. It requires a methodical approach, but there’s also an art to it. It requires vision (based on a high-level objective), iterative execution, as well as artistic foresight to represent the collected data in the best way possible for those who need to make use of it.

Data, Tech, & Black Communities (DTBC) is a group of diverse Black/Black heritage people working together to ensure data and data-driven technologies (e.g. AI) enhance Black lives rather than curtail or surveil them. We are made up of leaders of grassroots organisations serving Black communities, activists, technologists and practitioners. Our interest in technology is broad, but we focus primarily on the uses of data and data-centric technologies in the spheres of health, education, crime & justice, and employment & enterprise in the UK. If you’d like to learn more about EdTech, I would highly recommend reading this article by DTBC. It does a great job explaining the landscape of EdTech adoption and some government policies and funding initiatives in UK schools.

Are we asking the right questions? — Why? What? Where?

Working as a Data Analyst on DTBC’s Birmingham-focused EdTech project, one of my tasks was to collate demographic data about Black and Black heritage children (BBH) in Birmingham. My approach to any data collation exercise is first to ponder the project’s objective. This guides me when deciding which data elements or features are most useful. So, in my mind map for this task, I asked myself three basic questions: why do I need to collect demographic data on BBH people in Birmingham? What can this information actually tell me (and the rest of the project team), and where can I access it?

‘Why’ is relatively straightforward to answer. DTBC’s EdTech project “aims to identify and review EdTech tools used in Birmingham schools, evaluate the claim that the technology enhances student performance and ensure that the technology does not curtail or surveil BBH pupils in Birmingham”. And why Birmingham? The short answer is that it was selected during the planning phase because we don’t believe London truly represents most BBH people's experiences in the UK. The population census data showed that 49.3% of all Black* people in the country live in London, followed by the West Midlands, where 11.2% live. Delving further into the data revealed that 155,559 BBH** people constitute 13.59% of Birmingham’s 1,144,922 population.

While London has the highest concentration of BBH, we believe it would be an unrepresentative picture of BBH people’s experiences nationwide — London is that much of an outlier. During the project planning phase, project stewards selected Birmingham because London and the South East enjoy a disproportionate amount of national resources, and they believed that it would be reflected in schools funding, too. To this end, they felt that schools in Birmingham are more representative of those in the rest of the country.

My analysis of Birmingham’s demographic data confirms their hypothesis. It balances a relatively representative sample with a deep culture of diversity and community. Birmingham has a diverse ethnic scene, with 51% of its population being Black, Asian and other minoritised ethnic groups. It is also the local authority with the largest concentration of BBH people in England and Wales, according to the ONS census data from 2021. Ultimately, this is why Birmingham is the ideal location to execute this study.

Deciding what data you need

Now that we understand the ‘why’, let’s move on to the ‘what’. What data is relevant, and what can it tell us? What level of detail is sufficient? These questions are just some of the many that relate directly to how we manage the data collection process. Answers to these questions are informed by our understanding of project objectives and how and where we try to source the information we need. This project is focused on EdTech and BBH children, so understanding the landscape in which the schools within our scope (primary and secondary schools in Birmingham) operate is pretty important. In practice, that means understanding things such as which schools have the highest concentration of BBH pupils, what areas these schools are located in, and what levels of deprivation are in these areas. The answers to these ‘what’ questions help to contextualise other ‘what’ questions, including the details of what sort of EdTech tools and applications are being used in these schools and what, if any, discernible impact they have had on BBH pupils’ attainment levels.

Finding the data you need

Now for the ‘where’ portion of the process. Obviously, when trying to find the data that allows us to answer the ‘what’ questions, open data is preferable. Still, that doesn’t mean that this data is easy to find or that it’s in a readily usable format. There’s also no guarantee that the data you need is collected at all! This latter point is made really well by people like Mimi Onuoha in her project, The Library of Missing Datasets and organisations like the Centre for Public Data through their Missing Numbers campaign project. This FT article also does an excellent job of highlighting the small ‘p’ politics that shape these data collection decisions and the very real impact it has on policy and scrutiny:

“Open data is what the government wants you to know. Freedom of information requests are for what they don’t want you to know. The things you can’t FOI because they don’t collect them are what they really don’t want you to know” — Anna Powell-Smith.

The Office of National Statistics (ONS) population census data is a well-known open and public dataset, so it was an obvious starting point for sourcing the demographic data we were interested in. However, real-world data is often messy and incomplete, so validating the data from one organisation against multiple sources is crucial to ensure completeness and accuracy. It was necessary to augment the ONS data with data sourced from the Department for Education (DfE), such as the School Census data. This dataset gave us the list of Birmingham schools by ward and their ethnic group breakdown. Meanwhile, datasets like the English Indices of Deprivation (IoD) 2019 contained socioeconomic information about deprivation in different so-called Lower Super Output Areas (LSOA). Drawing on the contents of this dataset, we were able to sort and categorise the schools based on deprivation levels. This helped us answer one of the ‘what’ questions by helping us evaluate the relationship between schools with high concentrations of BBH pupils and the level of deprivation in the area.

I hope this blog post has provided some context for data collection and insight into how to approach it. In a subsequent blog post, I will discuss the intricacies of data collection during the first phase of this project.

I would like to thank Ade, Tracey and Mwenza, the project stewards for the DTBCs Birmingham-focused EdTech project, for reviewing and editing this blog post.

N/B: BBH people are categorised by the ONS’ ethnic group classifications of Black, Black British, Black Welsh, Caribbean or African and Mixed or Multiple ethnic groups: White and Black African/Caribbean. In contrast, Black people constitute the former of the two groups.

Decoding Data: Navigating the Art and Science of Data Collection for DTBC’s Birmingham EdTech… was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.

The UK’s Department for Education (DfE) has put in place various measures to drive schools in England to adopt or widen their use of EdTech. This is reflected in its 2019 Edtech strategy as well as its efforts to prime the Edtech sector ‘pump’ by providing subsidised training (at a cost of £14 million) for the Google Suite for Education and Microsoft’s Office 365 for Education platforms. It also, at a cost of £6 million, created ‘EdTech demonstrator schools’ through which twenty schools got between £75,000 and £150,000 to fund their EdTech projects. So there’s been a good deal of money sloshing around. What’s been lacking is any solid evidence — beyond a tech utopian confidence that it will work — of the benefits of EdTech and a comprehensive assessment of the potential harms.

What is Edtech?

Edtech is a term used to describe digital and data-centric technologies used within an educational setting. While the term is fairly ubiquitous, it is often used as a catch-all for digital and data-centric technologies that purport to support a wide range of education-related activities. Below, we have had a go at roughly categorising EdTech in line with these activities. We have limited our classification efforts to apps and activities that are relevant in a (primary or secondary) school environment.

These aren’t hard and fast or even exclusive categories. For example, apps like Capture Education and Class Dojo can be classified as belonging to the Direct Teaching Support and the Surveillance for Performance Monitoring Purposes categories. Many of these apps are multi-purpose, which can make them more attractive to schools and teachers. In some cases, once they are embedded in teachers’ ways of working, their lack of compatibility with other apps and, often the difficulty of transferring data out of specific apps and platforms, can make it very difficult for schools to switch to alternatives.

Why are we concerned?

Unfettered profit-making opportunities + too little regulation = a dangerous mix

Organisations providing Edtech have attracted billions in venture capital (VC) because of their profit-making potential. It’s worth asking just how Edtech companies hope to, ultimately, turn a profit and provide a return on the VC investment that many of them have accepted. The two prominent business models we’ve identified involve the sale of application licences and the harvesting of data. A report by the Digital Futures Commission (DFC) and 5Rights Foundation, paints a grim picture when it comes to schools’ ability to track data collection by Edtech apps and implementing or enforcing robust data governance. Despite this, a 2022 DfE-commissioned assessment of the UK EdTech market which also considered schools’ attitudes and practice in relation to Edtech found that only “a few respondents … noted the need to ensure that [Edtech] products had high quality cyber security and data protection approaches”. Perhaps this reflects the relatively poor job DfE has done in alerting schools to the risks. As further evidence of this, the use of impact assessments weren’t cited as a consistent practice by any of the respondents in the DfE-commissioned report.

DfE’s poor track record when it comes to safeguarding children’s data suggests it is not well positioned to enforce legal and ethical standards of practice for EdTech companies or guide schools on how to do so. Let’s not forget that DfE was happy to demand that schools begin collecting nationality data as part of the school census which it then shared with the Home Office right up until it was forced to stop. Back in 2019, following complaints from Defend Digital Me and Liberty about the DfE’s administration of the National Pupil Database (NPD), the ICO finally launched an investigation into DfE’s data practices. At the end of its assessment, the ICO made 139 recommendations for improvement to DfE. More than half of these recommendations were related to issues deemed urgent or high risk by the ICO (only one of the 139 recommendations was about a low risk issue). But the lessons obviously weren’t learned because in 2022, the DfE was reprimanded by the ICO after it was revealed that gambling companies had gained indirect access to the very sensitive Learning Record System (which contains records of people as young as 14) as a result of DfE’s failure to fulfil its duties under GDPR.

The DfE’s data governance failings have had seemingly no impact on its tech utopian perspective with regard to any data-centric innovation, as evidenced by the 2020 A-level exam fiasco — which disproportionately impacted poorer children and left home-schooled students with no grades.

Across the UK we are seeing the introduction of intrusive data-centric technologies like facial recognition in school canteens and fingerprinting in school libraries (1.28 million children as of 2014), under the guise of efficiency or safety. Brilliant organisations like Defend Digital Me and Privacy International as well as campaigners such as Pippa King work tirelessly to safeguard children’s data. However, there is no proactive, coherent government-led effort to introduce effective governance to safeguard children’s digital rights in schools and by extension, their futures.

There’s a lack of evidence of the benefits of EdTech (but the potential harms are clear)

Stories about the harms and misuses of EdTech, e.g the surveillance of children in schools and beyond and the resulting intrusive interventions, continue to surface from within the UK and across the world. However, robust evidence of the benefits that Edtech has actually delivered in terms of children’s educational outcomes is a little thin on the ground. Instead, we hear more about the potential of EdTech to improve children’s learning and lighten teacher’s workloads or increase their effectiveness. Given the documented, real (not potential) harms, it seems reasonable to ask that assumptions about the benefits of Edtech be unpicked, tested and verified.

Inadequate funding and digital poverty mean many children can’t adequately access EdTech resources anyway

Even if a more convincing case for the benefits of EdTech is made, there remain unanswered questions about the capacity of the government to realise those benefits for all children, particularly in the context of the current cost of living crisis and the growing evidence of ‘digital poverty’. Currently we just don’t know exactly how much of taxpayers’ money DfE has invested in its EdTech programming. But we do know that a year after making a promise to provide disadvantaged children with the laptops they needed during lock-downs, the DfE failed to fulfil its commitment, leaving thousands of children without much-needed hardware. Anyone following the teacher strikes will be aware that school leaders were told to fund the meagre below-inflation pay rise awarded to teachers from their overall school budgets. This is against a backdrop of declining per-pupil investment (in the state sector) which is now half of the equivalent per-pupil spending in the private sector; thanks to more than a decade of austerity. In choosing to fund Edtech, DfE is neglecting proven, cost-effective methods that improve educational outcomes across the board whilst creating the conditions for increased inequality.

What are we doing about this?

In summary, we are concerned that the unregulated use of unproven Edtech will reproduce and potentially deepen existing inequalities, especially for those affected by both digital poverty and structural racism. Many children could be excluded from accessing the potential benefits of technology, whilst being subject to its misapplications, with inadequate scrutiny or accountability in place.

We think parents, guardians and a host of others within communities want to understand how children’s data is being used as well as how the apps their children interact with are selected and vetted. We want to:

• build community with people interested in understanding how Edtech is being used in our primary schools;
• help develop community research skills in both quantitative and qualitative data collection and analysis in order to better understand what is going on;
• shed light on what Edtech is being used in our schools, which companies are selling them and understand what they are doing with our children’s data;
• understand the extent and impact of digital exclusion on children living in economically deprived areas.

We’re hoping to achieve these aims by focusing on two areas in the cities of Newcastle-upon-Tyne and Birmingham. Alongside community organisers we’ll be working with parents, guardians and anyone who wants to understand how the growing use of Edtech is serving children.

Our hope is that together we can gain a better understanding of the EdTech landscape, build bonds and deepen our data skills along the way. If you are interested in joining us on this journey, please get in touch data-tech-black-communities AT protonmail.com

References for newspaper headlines in cover image:

‘Woeful’ DfE blamed as betting firms gain access to children’s data | Department for Education | The Guardian

Educational inequalities in England barely improve in two decades, study finds | Financial Times

Facial recognition cameras arrive in UK school canteens | Financial Times

UK ditches exam results generated by biased algorithm after student protests — The Verge

School census

Is Edtech really improving outcomes for marginalised children? was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.

Image taken from BlackIllustrations.com with added text

In Autumn 2021, the Department for Digital, Culture, Media and Sports (DCMS) launched a consultation titled, Data: A New Direction. The consultation was dense, at 146 pages, but light on facts. It proposed reforms to weaken our current data protection law (GDPR). The government framed its proposals as necessary to “create an ambitious, pro-growth and innovation-friendly data protection regime that underpins the trustworthy use of data”. We believe that, as is often the case, those who are already marginalised will suffer disproportionately from the loss of these rights and protections.

In this blog post we explain why we are alarmed at these proposals. We also highlight how this consultation is part of a wider campaign, by this government, to significantly limit our existing rights and protections under law, as well as our capacity to object to discriminatory and unfair treatment. Finally, we share our thoughts on the need for our community, and others like it, to organise and fight against the erosion of our rights.

Why are we so alarmed by the changes?

Data protection law needs to be updated to keep up with technology; but not in the ways the consultation proposes.

Changes in technology can allow organisations to exploit legal loopholes in ways that can disadvantage or harm people. Data protection laws protect us from intrusive and inappropriate uses of our data and enable us to hold organisations to account if they are caught contravening accepted standards of use. That’s why it’s important that rules around how data about us is collected and used are updated so that new technologies align with societal values and expectations. However, several of the proposed reforms not only reduce organisations’ responsibility to respond to people’s queries about uses of their data, they also reduce the operational scope and independence of regulatory and enforcement agencies. As if that wasn’t bad enough, they remove restrictions on the ways organisations choose to reuse data about you. At present if such reuse falls outside the original scope of use, they have to ask your permission. However, under the proposed changes, they may not have to. Further, these reforms would give more powers to state bodies e.g the police to demand data about you from private sector firms without your consent. This will usher in even more state surveillance than we have now.

Let’s explore some real life implications of these proposed changes. The use of facial recognition technology is highly controversial and has prompted a lot of concerns, especially around its accuracy and intrusiveness. Despite this, North Ayrshire council in Scotland, procured a facial recognition technology system for use in school canteens. The justification was that this made the schools more ‘Covid secure’. Some parents were alarmed by the collection of their children’s biometrics for something as trivial as checking eligibility for school dinners. Their concerns were dismissed and ignored by the council. The parents were able to turn to both the Biometrics Commissioner and the Information Commissioner’s Office (ICO). It was only when these bodies with regulatory and enforcement powers stepped in and civil society organisations like DefendDigitalMe brought the case to the attention of the media, that the council backed down and the schools paused the rollout. The ICO is by no means a perfect regulator. However, if the Biometrics Commissioner role is folded into the ICO and the latter’s scope of operation is narrowed as is being proposed, who would these parents have appealed to?

Similarly, without the rights afforded under the current GDPR-aligned data protections regime, the Uber drivers who challenged Uber’s pay structures wouldn’t have been able to gather the data they needed to build their case. The resulting Supreme Court ruling has made it possible for gig workers (who are disproportionately Black and Brown) all over the UK to push for better conditions. Data rights impact so many aspects of life.

The Brexit excuse– the lie that gutting our data protection rights will spur innovation

In understanding how we got here, it’s impossible not to mention the Brexit project. Many Brexit supporting MPs have viewed Brexit as a “one off chance for the UK to escape EU ‘red tape’”. Last year a government taskforce on innovation, growth and regulations (TIGRR) proposed GDPR should be scrapped and replaced with a ‘UK Framework of Citizen Data Rights’. The framework is worryingly light on detail but the taskforce insisted it would give people more control over their data and support growth and innovation. Well, if you believe that, we have some art doodles that we’ve converted to NFTs to sell you! The erosion of our data protections is not a prerequisite for innovation. Does the requirement to have seatbelts prevent car makers from innovating? Unless, of course by ‘innovation’ we mean reckless, irresponsible experimentation without accountability. We only have to look at the behaviour of tech companies in low income countries with limited data protections to see what that looks like.

Our experience of responding to the consultation

Rather than the government exploring the negative externalities of its proposals and presenting mitigation steps, its consultation placed the burden on respondents (mostly from civil society). The DTBC team spent many late evenings after work poring over the 300+ questions in the consultation, collating relevant stories, doing research* and writing our submission. We did this despite the sinking feeling that the government will ignore well-evidenced arguments against their proposals as they have done with various controversial bills. However, we also recognise that failing to formally state our objections gives the government room to imply broad support for their reforms.

At present we are tracking and monitoring a number of consultations on proposals to scale back existing rights and protections which also have a data and technology component to them. For example, the Human Rights Reform proposal which is a clear attack on human rights and the amendment to the Official Secrets Act which makes it easier to silence journalists. While the Police, Crime, Sentencing and Courts bill, Nationality and Borders bill and the Online Safety bill pass through houses of parliament, we will be highlighting how data-centric technologies could be used to enforce them and, where we can, opposing these bills.

This work takes time and it can’t be done well by a tiny team of three people; for example, we couldn’t answer all the questions we wanted to in the DCMS consultation. We need the wider DTBC community to get involved too.

What happens next and how we hope members of DTBC should get involved

The relentless pace of consultation releases sometimes feels like a war of attrition being waged on civil society. It’s exhausting but those amongst us with the capacity must continue to resist and protest. As Black Protest Legal Support UK have explained, if the government manages to get its way, many Black people and others from marginalised communities in the UK will find it that much harder to survive and thrive.

If you have been moved by anything we have written in this blog post, do please get in touch data-tech-black-communities[at]protonmail.com

Note *The intimidating breadth of this consultation led various civil society organisations including: Open Rights Group, Open Data Manchester, The Legal Education Foundation to create explainers and host discussion events to support wider civil society to get to grips with the government’s claims. We benefited from their hard work and if you’d like to get a deeper understanding of the proposed changes, these explainers are a good place to start.

The government is trying to erode our data rights and protections. We must resist. was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.

You’re a funder or research organisation working with a marginalised community. You have good intentions. How can you make sure you support and don’t extract, from marginalised communities?

Between December 2020 and May 2021, we interviewed participants, convened roundtables, and ran events with and for practitioners, technologists, academics, and community leaders working with Black communities. Our aim was simple: develop an understanding of how to make data and technology have a more positive impact on the lives of Black people in the UK. Along the way, we asked: how do we share what we’ve learned so that other marginalised communities can benefit?

By the end of our work, we’d learned lessons about making interactions more holistically positive for everyone involved. We’ve set out three lessons and recommendations that we think are most relevant to funders and research organisations.

To discover broader lessons from our exploration, we shared ‘Lessons from our exploration: How data & technology impacts UK Black communities’.

What’s a marginalised community?

Marginalised communities are groups of people excluded from full participation in mainstream life. This means they don’t have full and equal access to the social, economic, educational, or cultural opportunities their societies have to offer. A community might be marginalised due to one or more factors such as their race, how they identify their gender, who they love, their age, physical ability, language, if they’ve been to prison, are working class, or are immigrants. It’s worth noting that people from marginalised communities don’t have a monolithic identity; they can identify as white, Asian, disabled, LGBT+ or a combination of these and a number of other identities.

Lesson 1 — People & Power Dynamics

We believe that understanding and addressing the impact of data and data-centric digital technologies is a long-term endeavour. And so, we recognise the need for strong networks and not just specialists and superstars.

What does this mean in practice?

1. Connect people in ways that work for them:

We found that participants at our events were most responsive when engaging with others with similar experiences, expertise, and values. These sorts of engagements led to the creation of relationships beyond the initial event.

Connecting people means not simply seeking out the most visible, vocal people . They may claim to speak for, or convene Black communities but might simply be interested in furthering their own platforms.

Connecting people effectively also means using your platform and power to reduce the burden of continuously being researched on topics that are already well known. It can sometimes seem like marginalised communities are involved in an endless conveyor belt of research projects without the corresponding actions or change.

2. Make the interactions relevant to those involved:

Experts and specialists play an essential role in helping participants see the links between their lived experience and the bigger picture. We found that stories were the best vehicle for achieving this.

Empirical data tied to real human stories — taken from popular culture or UK-based news stories — are compelling. Stories are even more effective when shared by people with relevant or similar experiences.

3. Preempt and address the knowledge gaps:

We were eager to give every one room to share at our roundtables. We recognised that if the knowledge gap within the group was too pronounced, it could discourage participation: exchanging thoughts, ideas, and relevant experiences.

To resolve this, we made sure everyone had a baseline of knowledge by preparing and distributing primers on the topics discussed ahead of every roundtable: education, crime & justice, health and employment & enterprise.

Lesson 2 — Equitable Funding

What’s the best way to understand the impact of data and technology on the lives of any group of people? Track the impact within the context of their daily lives over time. The impact of technological change can take a while to manifest. Grassroot organisations embedded in community life are best placed to do this vital tracking work — and do it in a profoundly contextual way. However, tracking is time-consuming and labour-intensive work, usually peripheral to the core mission of these organisations.

We learned that few grassroots organisations are funded to continuously monitor the impact of data and technology on their communities. This is despite the huge role technological changes are having on our society. We know that this evidence is needed to understand technology’s growing impact. Empirical tracking of trends and developments needs funding to flourish.

We believe this funding must be available to track developments and trends in the data and technology space. We also believe this funding must go to organisations working within and with marginalised communities, not only to those outside it (for example think tanks and research organisations).

How can you make funding more equitable when working with marginalised communities?

1. Fund organisations within communities to do data and tech research and provide interventions:

This helps build and keep skills and knowledge within marginalised communities and the organisations that work directly with them. Retention has two positive impacts: it directly contributes to/reduces the representation problem, and it discourages extractive practices that don’t centre the community or prioritise equitable enrichment.

2. Pay people for their time:

Marginalised communities are more likely to be financially pressured, working-class, or otherwise economically disadvantaged. Asking people to volunteer limits the pool of people who can participate, leading to a similar problem we touched on in lesson one — research conducted on the most visible, vocal, and least pressured members of marginalised communities.

3. Fund community organisers:

Keeping track of developments and trends in the data and technology space is labour- and time-consuming, so it needs proper funding. We believe it pays off in reduced duplication of effort for researchers and more robust networks within and between marginalised communities.

4. Fund core infrastructure:

Data and technology research requires community researchers to have access to IT and the internet; to have a suitable working environment; to have an appropriate living environments. Funding needs to contribute towards core costs, not just the cost of running a project and ignoring the cost of the infrastructure that research relies on.

The change we want to see is data and digital technologies that enhance Black lives (and indeed the lives of all in marginalised communities). Equitable funding helps achieve this.

Lesson 3 — Data About Us, For Us

We learned the importance of thoughtfully ensuring equitable access to data: from raw data produced as part of work done in marginalised communities to the final products of work done with marginalised communities.

Pervasive data collection is becoming the norm, but how data is collected and used isn’t equitably shared. Lack of skills and access to data directly affects the ability of marginalised communities to advocate using data. It also means that the ‘shape’ of the data reflects the priorities of those collecting it. Very often, data about marginalised communities isn’t available for them.

Just as marginalised communities can be excluded from the design of research and the knowledge creation process, they are often denied access to the underlying data that has been collected about them. This is extraction in practice. We heard frequent stories of researchers putting out calls for Black participants to attend their workshops or seeking the help of community leaders to convene data-gathering workshops, and then failing to share the curated data or other work output with them. At most, conscientious researchers share a copy of the final report.

Why does this matter? The focus of a finished report will reflect the priorities of the funder or of the research and not necessarily those of the community the work is based on. Sharing the data (and models) provides a tangible asset that grassroots organisations and communities can use to inform other projects and initiatives. And to use that data, they’ll need skills as well as capacity. “Data about us, for us” is more than just access to data; it’s about co-developing research with communities and ensuring the critical skills to get the most out of data.

The benefits of “data about us, for us” don’’t end there — as more quality data grows in a community, so does its overall value. Deep, quality data provides a fuller and more comprehensive picture of changes within the community. It offers a sound basis to take action and support sustainable, long-term change.

While the mere existence of data doesn’t guarantee change, its absence is often cited as reason for inaction. By sharing data and related assets with grassroots organisations, funders can support communities becoming curators of their own data.

How can you help communities to retain and sustain data, knowledge, and skills?

1. Reduce pervasive data collection:

Encourage organisations to take safe, ethical, and user-centred approaches to data collection. Where data exists, encourage reuse of what’s already collected.

2. Mandate data sharing:

When the Gates Foundation first mandated open licensing of data and materials they funded, it was a game-changer. Funders can make a big difference to the availability of data that is safe to share by mandating safe data sharing, having clear policies about licensing, and ensuring policies are put into practice.

3. Fund grassroots data collection:

Engage organisations working with marginalised communities to collect more of this data themselves. Doing so improves the skills and knowledge of using data within marginalised communities. Alongside strong networks, it reduces repetitive and duplicated data collection, which overburdens communities. An excellent example is Account Hackney, a youth-led social action project based in Hackney, London.

In Summary?

Through our work with Black communities, it became clear to us that while there is value in demanding greater transparency and access to data collected about communities, there are other ways of ensuring that “data about us is also for us”. The three ways we’ve listed are a good starting point for organisations with no cohesive policies to start preventing extractive data practices.

Extractive practices do not build communities. Marginalised communities are frequently observed and researched; however, the benefits do not trickle down to the communities themselves. Currently, the fiscal focus of research funding is often organisations without any ties to or investment in the marginalised communities being researched. Our recommendations seek to shift power and funding, increase equity and re-centre power in marginalised communities. We believe this is the best route to the lasting and sustainable change that most funders genuinely want to see.

You can find out more about the work of Data, Tech and Black Communities here: Who we are, what we value, and the change we want to see.

Recommendations: How to support rather than extract from marginalised communities was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.

Lessons from our exploration of the impact of data and technology on Black Communities in the UK // Edit by Edafe Onerhime for Data, Tech, & Black Communities // Licensed CC-BY4.0

Exploring the impact of data and data-centric technologies on Black communities in the UK

Black communities in the UK are diverse and muli-faceted, so too are their experiences of data-centric digital technologies. That’s why we wanted our exploration of the impact of data and technology on the lives of Black people here to be wide and anchored in real-life experience.

In adopting this approach, we wanted to move away from the high-level aggregate view (peppered with a few individual stories often set in the US) that is often applied to discussions about data-centric technologies. Our suspicion was that the latter tends to present the impact of these new technologies in fairly abstract terms which leave people feeling vaguely unsettled but not empowered to do anything about it.

We also felt that this aggregate view fails to account for the fragmented reality that many of the current crop of data-centric technologies create. In other words, different groups of people experience different versions of reality online. This doesn’t mean that there aren’t commonalities across experiences. However, it does mean the ‘aggregate’ view doesn’t really capture most people’s experience of engaging with data-centric technologies. We suspected that only by first understanding the variety of experiences can we talk about data and technology in terms that most people relate to, can we begin to point out these commonalities. And once that becomes clearer, it becomes easier to see where interests align across groups, and to support group-led efforts for collective action.

For all these reasons, we tried to design our exploration of data and data-centric technologies in ways that are relevant to the lives of the practitioners, organisation and community leaders and others that we were hoping to work with. Findings from our roundtable conversations can be viewed in this presentation and you can learn more about the principles that guided our efforts and what we were hoping to achieve by reading our blog post titled, Who we are, what we value, and the change we want to see.

Now that we’ve explained our approach to, and rationale for this work, let’s outline some of the lessons we learned.

Many of the issues with data-centric technologies are structural

Over the course of the project, we got the chance to have a lot of conversations. It soon became apparent that many of the factors shaping Black communities’ experience of data and data-centric digital technologies and their ability to address emergent issues, are structural. We felt it was important to share the lessons related to these issues with funders and research institutions who have the structural power to do something about them. So we wrote a blog post about some of those lessons: Recommendations: How to support rather than extract from marginalised communities.

Black protestors stand with placards by Olya Abdugalieva // Edited by Edafe Onerhime for Data, Tech, & Black Communities.

Lessons for the community of impact we’re helping to build

But we are not without agency and there are lessons we can apply to our current ways of working that could make said agency more effective. By ‘we’ I mean the practitioners, community organisers, grassroot organisation leaders, academics and technologists that are part of this community of impact (CoI)* that’s working toward uses of data and technology that improve and celebrate life.

*A CoI is a group of people who work towards a shared passion or cause, and learn how to do the work better through interacting and sharing knowledge — Institute of Global Homelessness.

So, below, , in no particular order, are some of the other lessons we learned.

Raising awareness is not enough.

One of our assumptions when we started this work was that many people are largely unaware of the harmful ways that data-centric technologies can affect us. This harm falls especially hard on those of us who are from low income households because economic disadvantage tends to further compound the effects of other forms of marginalisation.

There are good reasons for this lack of awareness. For one thing, the use of data-centric technologies is increasingly pervasive and often undeclared, so if it isn’t your day job, keeping track of trends and developments can be tricky. For another, the effects of these technologies can be diffuse and occur some time after the products and services are deployed. Alix Dunn sums up the negative impact of emergent technologies perfectly when she describes it as ‘slow violence’.

The interviews and discussions we engaged in helped us see that compiling facts about, and highlighting the harms of data-centric technologies on, Black lives aren’t enough to achieve our stated goals. We needed to explain how these harms tangibly affect people’s lives. Also, people need the time and space to discuss, question and reflect on what this means for them, their children and other people they interact with as well as time to figure out what to do about it. This is why it’s important for us to continue to engage with and support organisations that are invested in marginalised communities and who have the social infrastructure to support these discussions and help people take action. This understanding has been a key driver of our proposal to carry on this work as part of a community of impact.

It’s important to understand who we need to work with to improve the ways in which data and data centric technologies are used in our lives.

Social media has proven an effective way of quickly transforming, otherwise disconnected, individuals into a coalition capable of pushing for a specific change. It works especially well when the change being pursued is relatively simple to define and the desired outcome(s) can be achieved quickly. In these conditions, the coalition’s breadth and longevity don’t matter so much. At the very least, they are manageable challenges.

However, reversing and preempting the kind of ‘slow violence’ we’re looking to tackle with regard to so-called ‘negative externalities’ of data and technology requires different tools. We need networks of people with diverse talents and experiences that are capable of focused, sustained, nuanced and multi-faceted action.

It’s important to foster an environment of psychological safety.

Structural racism allows tech companies (and the institutions that buy and integrate their products) to remain incurious about the impact of their products and services on Black communities and to blithely disregard negative effects when they are pointed out.

For example, the Home Office was aware that the face detection technology they implemented for checking passports photographs online worked sub-optimally for people with darker skin tones. However, having satisfied itself that the photo-app worked sufficiently well for the majority it decided that negative outcomes for ethnic minority users was an acceptable trade-off:

“User research was carried out with a wide range of ethnic groups and did identify that people with very light or very dark skin found it difficult to provide an acceptable passport photograph…However, the overall performance was judged sufficient to deploy.”

Unpicking and analysing the effects of structural racism in yet another sphere of life is emotionally exhausting. Turning what could be a relentlessly draining exercise into something more empowering requires an environment of psychological safety. At our roundtables we encouraged active listening to one another’s experiences. At the start of every session we reiterated our expectation of and commitment to mutual respect.

We discouraged the use of overly broad generalisations and ‘devil’s advocate’ type argumentation as a means of facilitating deeper understanding. Instead we favoured good faith questioning and discussion along with a commitment to shared learning. Outside our roundtables because we were aware of the risks associated with hypervisibility for marginalised groups, we promoted and conducted our discussions off social media.

Challenging tech power requires a whole system approach.

Facilitating and amplifying the agency of organised people was a core driver for us. However, it’s disingenuous to suggest that ‘empowerment’ alone can address the kind of power imbalance that is often at play. It is also misleading to suggest that this is simply down to tech companies (Big Tech or otherwise). It would be impossible for data-centric technologies to be as pervasive as they are without both public- and private-sector organisations embedding their use in their services.

For example, the use of so-called EdTech products and services, created by private sector technology companies, has grown within mainstream education. This can be linked to the Department for Education’s (DfE) policy decision to actively promote the use of such products. It has done this without instituting a robust regulatory framework for assessing and certifying these products and services nor has it created guidelines for schools about how to do so. Further, while encouraging the adoption of these products and services, it hasn’t consistently or comprehensively dealt with the problem of digital exclusion amongst a significant minority of school children in the UK. The blame for any widening of the attainment gap as a result of this policy can’t be placed solely at the feet of tech companies.

It’s also worth noting that without permissive regulators these institutions and corporations would be far more circumspect in their use of such technologies. As it is, investigative journalism, worker mobilisation and legal action (or the threat thereof) have yielded better outcomes for those who have suffered harm as a result of the misuse of data and data-centric technologies, than complaints to the regulator.

Data and data-centric digital technologies also support greater inclusivity.

Poorly designed or inappropriately used data and data-centric products can have big, negative impacts on the lives of individuals. That could be wrongful arrest or being kicked out of a Southern Co-Op store because of unregulated use of biased automated facial recognition. Alternatively, it could be proctoring software wrongly flagging ethnic minorities as potential cheats. With such potentially high stakes it’s easy to focus on the negative impact of these technologies on Black communities. However, homing in on just the negative impact of data-centric technologies denies us the chance to explore opportunities for flourishing. This, in turn, stymies our proactivity, placing us in a permanent reactive state. That’s why we are so inspired by the work of organisations like Afrotech and why we are keen to continue to work on the emergent themes from this work. Our first project is likely to focus on the theme of data about us,[needs to be] for us.

You can find out more about the work of Data, Tech and Black Communities here: Who we are, what we value, and the change we want to see.

You can download and re-use our posters which are licensed under CC-BY 4.0.

Lessons from our exploration was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.

Can we escape the consequences of algorithmic injustice?

1. When I’m prevented from attending my university of choice because the algorithms used to decide A-Level results disproportionately penalise marginalised groups that I’m part of?
2. When I need to use facial recognition technology to sign in to work but it doesn’t recognise my brown skin and locks me out of work?
3. When I’m unjustly racially profiled and added to the Metropolitan police’s discredited Gangs Matrix?

An algorithm is “A finite set of unambiguous instructions that, given some set of initial conditions, can be performed in a prescribed sequence to achieve a certain goal and that has a recognizable set of end conditions.” — freedictionary.com

Algorithms that are used in the types of machine learning systems described above all learn from data that is socially and historically constructed.

Algorithms are not just simple pieces of code, they’re being embedded in more and more important systems and by extension, altering our social fabric by drawing on and amplifying human assumptions that are generated by humans (and embedded in datasets on which they are trained on). Portraying an algorithm as a simple piece of code that is wholly neutral and its predictions as wholly rational, disregards the assumptions and social contexts that make the code what it is.

In all three examples of harm mentioned above there is a dominant narrative of efficiency that drowns out voices calling for more equitable approaches to decision-making. Professor Ruha Benjamin beautifully articulates the shortcomings of this approach when she notes that prizing efficiency over equity produces containment. More often than not it is already marginalised groups that are ‘contained’. Much is made of the transformative potential of data. However, when its collection and curation mirrors the same extractive processes applied to resources like oil, producing capital for a few and not for all, how can we make the most of the promises of the digital age?

Gambling Futures

In 2020, Ofqual decided that algorithmic decision-making was the best approach for deciding A-level grades for pupils who’d been unable to sit conventional exams on account of the ongoing pandemic lockdown. They decided on this approach instead of so-called Centre Assessed Grades (CAGs) because it addressed the “unprecedented increase” in higher A-level grades in 2019 (“CAGs at grade A and above were 12.5% higher than outcomes in 2019” ). In other words, algorithmic decision-making provided an ‘objective’ way to counter the threat that this inflation posed to the “market value” of higher grades. Furthermore Ofqual believed their preferred approach was fairer than accepting CAGs; they reasoned that:

“accepting CAGs would also mean that any leniency or severity in the CAGs submitted by individual schools and colleges would not be addressed. This would make it easier to get a grade at one school or college than another leaving unfairness between schools and colleges.”(Quote — A-Level Awarding explainer by DfE),.

Ofqual was interested in addressing some forms of unfairness, it just didn’t consider all forms of unfairness as equally important. The key criticism of Ofqual’s algorithmic approach was that it penalised some high-performing students who attended schools with historically poor performance. This ‘quirk’ of the algorithm which would only affect pupils from marginalised groups was known and government ministers were informed but the decision was made to press ahead. Ofqual and government ministers chose to be guided by their success criteria that the attainment gaps between groups should not widen [as a result of their model] (OSR Review, 2021). Erstwhile head of Ofqual, Roger Taylor remarked at the time “Some students may think that, had they taken their exams, they would have achieved higher grades. We will never know.”

In a similar vein, when assessing the equality impact of its approach to awarding vocational qualifications Ofqual recognised that patterns of attainment in the recent past, including any inequalities would be replicated. (OSR, Review Page 88).

It is telling which forms of unfairness Ofqual and government ministers were determined to address and which (more systemic) manifestations of unfairness it was willing to overlook.

Obviously this had implications for many students from marginalised backgrounds who were hoping to go to university but it doesn’t end there. The pandemic is having an unequal impact on school children. Many children from families with low incomes have lacked access to technology that would have made remote learning a viable option. A significant minority have struggled to access adequate nutritious food. Any decision-making that fails to take account of these structural disadvantages is not fair and any talk of equality is a smokescreen. In a society as deeply unequal as ours, we need frameworks that consider equity and not just ‘equality’. And this understanding must inform our approach to designing and selecting algorithms. Otherwise, we risk reinforcing existing forms of oppression.

While Ofqual and the Education Secretary, Gavin Williamson eventually backtracked on the use of algorithmically-generated grades, for many students the damage had already been done. Many lost university places and scholarships that were conditional upon their official grades before the policy reversal was implemented. A generation of students and their parents/guardians have now experienced algorithmic decision making first hand and have shown policy makers that decisions about individuals which are based on aggregate historical data is not always appropriate.

Preventing algorithmic harm

Algorithmic injustice is defined as the dispossession caused by the automation of existing or new forms of discrimination or harm towards an individual or a group.

Algorithmic bias sits within algorithmic injustice. Bias can enter anywhere within the algorithm’s lifecycle, as explained by AI researcher, Deborah Raji. Everything, from the underlying assumptions of the modeller to the ecological cost of large-scale models , needs to be critically reviewed with a view to preventing harm. The causes of algorithmic harms can’t be reduced to just the interpersonal (e.g biases of the designer), instead a socio-technical lens must be adopted.

What steps could we take?

So far we have considered some of the forms that algorithmic injustice can take in real life. The question we now have to ask ourselves is: what can we do about it? Below I explore some of the ways ‘we’ can do this. This is a broad and diverse ‘we’. Our communities of resistance must be diverse and operate on many fronts in order to tackle this multifaceted problem.

Algorithmic Auditing: Spotting the problem

Algorithmic auditing is the process of reviewing an algorithm’s design, its code and its output in order to assess its impact in as holistic a way as possible. Effective audits adopt a number of techniques in order to properly assess an algorithm’s potential effects, for example, interviews and workshops with employees.(The Markup). Algorithmic auditing has enabled researchers to challenge organisations’ decisions. For example, it was this type of auditing conducted by Joy Buolamwini et al to flag the problems inherent in many facial recognition software with regard to darker skinned women (Gendered shades project). This led to improvements by some organisations.

Despite the obvious benefits of algorithmic auditing, it hasn’t been smooth sailing. Some corporations refuse to cooperate with researchers, forcing them to implement more labour-intensive approaches. Other organisations seek to force researchers to keep their findings private. For example, Ofqual asked Royal Statistics Society fellows to sign a non disclosure agreement (which the RSS refused to do) in order to be granted access to its now infamous A-level grading algorithm.. Furthermore, auditing can be used as a public relations cover by companies without any serious efforts being made to act on the resulting recommendations. (HireVue’s audit of hiring algorithm seen as a stunt).

Algorithmic auditing isn’t a silver bullet, especially if it’s left to companies’ discretion. Experience has shown that in these circumstances organisations will act to protect their profit margins. This is why it’s so important for us to view calls for de-regulation with healthy scepticism. Rather than stifling innovation and healthy competition, effective regulation can be a force for protecting consumers and the social fabric of our society.

GDPR: Rights of the data subject

Enacting our rights under the General Data Protection Regulation (GDPR) can also be a safeguarding mechanism to prevent, or seek redress in the face of, algorithmic harms.

The non-profit organisation, Worker Info Exchange, which represented Uber and Ola drivers in their respective legal cases were able to do so thanks to the provisions of e Article 22 of the GDPR. This portion of the legislation relates to people’s rights with regards to decisions made solely by machines i.e. automated decision-making. In this particular case, Uber and Ola drivers were challenging cases of unfair automated decisions that impacted their pay, performance ratings and in some instances led to job dismissals (Worker Info Exchange, 2021).

The Dutch and UK courts ordered transparency for algorithms used in performance management and required Uber to reinstate the workers who were unfairly dismissed by algorithmic means.This case demonstrates how GDPR can help to fight against some manifestations of algorithmic injustice. This is why it’s important for us to resist recent proposals (by the Taskforce on Innovation, Growth and Regulatory Reform led by Sir Ian Duncan Smith) to do away with GDPR post-Brexit.

However GDPR does have it’s weakness, one of the cases brought against Uber was rejected by the courts that Uber’s decision to dismiss some of its drivers was not ‘solely automated’- a key condition of Article 22. Furthermore, despite the rights granted under article 15 –a right to access data — Uber was able to resist requests for all the data it held about its drivers because that data was linked to data about others i.e. passengers. This highlights another weakness of the current iteration of GDPR — its focus on /an individual rather than a group/societal view of data. Overall, law is fluid and needs interpretation the court decision for transparency, isn’t enough in itself to remove forms of oppression, it simply helps to reveal them.

Some legal scholars have different views as to the most appropriate legal frameworks for addressing algorithmic harm. For example, Monique Mann and Tobias Matzner argue that anti-discrimination law could play a better role against algorithmic profiling. The implication being that current legislation needs to go further to protect marginalised groups and wider society from algorithmic harm. It is important for marginalised communities to organise and begin to demand these changes if we want better futures that reduce the incidence and impact of algorithmic injustices.

Data and power

Generative uses of technology and data

So how can we build equity in the design of data-driven technologies and decision making?

One of the overarching approaches or frameworks that might help us think about how we achieve this objective is ‘generative justice’:

“The universal right to generate unalienated value and directly participate in its benefits; the rights of value generators to create their own conditions of production; and the rights of communities of value generation to nurture self-sustaining paths for its circulation .”(Eglash, R (2016)).

In the case of data and data-centric technologies this means, data about us should be for us. We are data workers every time we engage with digital platforms or fill out administrative surveys. We share our data knowingly (or unknowingly) and our data is transformed into value for private and public sector organisations. Value generation does not begin and end with a data scientist, it begins with the people who the data is about.

Data stewardship

A data trust “provides independent, fiduciary stewardship of data’. Trustees look after how data is used. Inspired by common law practices of a land trust; the idea of a data trust is to pull collective rights of consenting individuals (data subjects) to be advocated on behalf of a data trustee. — Ada Lovelace

Both auditing and legislation empower by preventing harm or facilitating redress after harm has occurred. This is important but insufficient for full flourishing. We also need approaches that enable more equitable distribution of power in the first place. Let’s consider, those Uber drivers again. In response to one of the Dutch courts’ rulings, Workers Info Exchange pointed out that the “burden of proof on workers to show they have been subject to automated decision making before they can demand transparency of such decision making.” is unfair (ref). This position sustains the power of the institutions rather than the workers, ideally the burden of proof should be on the companies.

Would having more say with regards to how their data is used by their employer introduce greater better accountability on the part of the latter ? I think there is a strong argument for believing that this would be the case. Data trusts are one way that these workers can positively increase their power by asserting greater control over the use of data about them.

Worker’s info Exchange is establishing a data trust, to provide collective means for gig workers to exercise their rights under GDPR. Taking companies to court can be extremely expensive. Acting collectively is more effective and systems such as data trusts help to build the type of solidarity that makes this type of cooperation more likely. Similarly, the organisation, Driver’s Seat has created a data co-operative in which drivers upload their data and this e is used to generate insights about their working patterns which are then made available to them. It’s also sold to interested parties, with the profit shared amongst the co-operative members

You may be wondering why I have highlighted a case study that focuses on workers’ rights, rather than on racism. You can’t separate class from race. Well, for one thing, people of colour are over-represented in Uber’s workforce so there is a racial component to this case as its material impact falls disproportionately on some groups. Also, this particular case study provides useful insights and lessons about how data-centric technologies can be deployed in ways that further marginalize people with who are already disempowered.

Creating counterdata

While data stewardship models facilitate better control and transparency with regards to access to important data sets, they don’t necessarily address the process of data creation or the quality of the resulting data. How and why data is collected matters too. To help make this point, let us consider the following question: is it possible to build an “anti-racist” ‘gangs matrix’?

In the context of the UK, the answer is no. The very conceptualisation of ‘gangs’ is highly racialised. In their State of the Nation report, the Runnymede Trust discuss the racialisation and criminalisation of ethnic minorities (pg 66) thus:

“The concept of the gang and its relationship to ethnic minority groups, particularly Blackness, has an extensive history in Britain (Gilroy, 1987a; Keith, 1993). Williams and Clarke (2018) have argued that the more recent Minority ethnic groups, policing, criminal justice system focused by policymakers and politicians on the gang has resurfaced after the social unrest in England in the summer of 2011. Labelling the social unrest as a product of an endemic gang problem in the country led to a flurry of anxieties where ‘the media, politicians, think tanks and academics were quick to evoke the already established view of the gang problem’ (p.7)”(Runneymede 2020)

Given this social context, it’s no surprise then that the Met Police’s Gangs Matrix unjustly racially profiled athousand Black men, whose names had to be removed from the database in early 2021. This is why it’s so important for us to understand how power manifests in the social constructions of the databases that fuel the modern ecosystem of machine learning systems. Technology won’t build “safer” streets if it simply further marginalises certain communities.

Collecting counter data provides a means of scrutinising the ways in which power operates when it comes to data creation. It does this by presenting an alternative view of the same set of interactions. This is what Account, a Hackney-based, youth-led police monitoring group did with their report, ‘Policing in Hackney: Challenges from Youth in 2020’.

Creating counter data helps us to point out weaknesses in current processes of data collection and curation (as well as in the legislation that underpins them) . It isn’t an end in and of itself but it can provide communities with data they desperately need to begin to address local challenges in a more informed way. It can also serve as a good basis for better conversations and a way of reimagining better futures.

Conclusion

There are many ways to tackle algorithmic harms, from insisting on greater accountability from the corporations that release these products and services to data nuions and the creation of counterdata. Each approach has strengths but no single approach can tackle all the facets of algorithmic injustice. Only by fighting this problem on many fronts i.e combining approaches, will we be able to actualise generative justice.

Supporting one another in “communities of resistance”, can help us to learn from one another and work more effectively. I want to see a world in which data about us is truly for us.

Communities of Resistance: Fighting Algorithmic Injustice and Building Better Futures was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.

Written by Tracey Gyateng, Ade Adewunmi, and Edafe Onerhime.

*polemic: an aggressive attack on or refutation of the opinions or principles of another. “Polemic.” Merriam-Webster.com Dictionary, Merriam-Webster,. Accessed 8 Apr. 2021.

In May 2020, George Floyd was murdered by police officer Kevin Chauvin in the United States. The horrific killing sparked protests in the US and all over the world. Many of the protests outside of the US were a show of solidarity with African-Americans but also sought to draw attention to anti-Black discrimination and violence in their own countries too. That was definitely the case in the UK.

In July of the same year, in response to Black Lives Matter protests in the UK, Boris Johnson announced the creation of an independent commission to examine racial inequality in the UK. The Commission led by Dr Tony Sewell, an education consultant and charity leader, was set up by Munira Mirza, a close aide of the PM. There was disappointment from several quarters about the creation of this commission. For one thing both Sewell and Mirza had previously expressed scepticism about the very notion of structural racism in the UK. For another, there have already been several reviews of racial disparities across a wide spectrum of UK life and successive UK governments have been slow to act on the resulting recommendations.

Despite this, when deciding what the focus of our project to examine the impact of data and data-centric, digital technologies on Black communities should be, we chose to mirror the focus areas of the Commission on Race and Ethnic Disparities (CRED). We felt it might be interesting to compare the narratives that emerged from both works.

Structural racism is real, now let’s move on

Having said that, CRED wasn’t the impetus for our work. Rather, it was our understanding that, without proper safeguards, innovation will often entrench existing power dynamics and economic (dis)advantage. Still, as three middle-class women working in tech, we were aware that our perspectives and experiences couldn’t possibly capture the variety of experiences that Black people have while interacting with technology on a daily basis.

And rightly so, Black people in the UK aren’t a monolith. We felt an exploration of these diverse experiences was a worthwhile project. That’s the short story of how in January of this year we kicked off an exploratory review of the impact of data and digital technologies on the lived experience of Black people in the UK within the spheres of: education, health, crime & justice and business & enterprise.

Our project was well underway by the time the Sewell report was released and we already had enough evidence to suggest that our understanding that innovation (in this case data-centric technologies) can and does entrench inequality wasn’t misguided. The conclusions of the Sewell report made for disheartening reading; it didn’t reflect our project findings, our lived experiences, or those of anyone we knew. Its sole purpose seemed to be the provision of cover for government inaction.

We might have stayed in that despondent state but for an epiphany triggered by political events at the time. More specifically, two news stories. One was about an alleged affair between Boris Johnson and his ‘business associate’, Jennifer Arcuri as well as the possible misuse of public funds. The other was about former prime minister David Cameron who, it was revealed, had lobbied the Chancellor, Rishi Sunak on behalf of Greensill Capital. The press coverage was superficial and relatively muted when contrasted with the treatment of other politicians who’d committed far less significant misdemeanors. The artist Cold War Steve, captured this really well in his work featured above. It occurred to us that the image could also be considered an allegory for the Sewell report. In other words, the report serves as a great distraction from the material conditions of people of colour in the UK. Instead, it keeps us debating the existence and impact of structural racism.

What is needed, and has been for some time, is a commitment to act. And while a further inquiry could have been helpful in sourcing and curating the most up to date research and setting out clear recommendations for the current government, its failure to do so does not negate the reality. And that’s where we exist — in a lived reality, and data and data-centric technologies are having a broad and very real impact on that reality. We felt that it was important to address the facts on the ground; more important than debating the alternative reality that the report seeks to project. Especially since others such as the Runnymede Trust have already done that so brilliantly. So we decided to forge ahead with the work we’d already started.

What does this report have to say about the impact of data and data-centric, digital technologies on Black Communities?

In two words — very little. Only two of the report’s recommendations specifically focus on data and data-centric technologies such as artificial intelligence (AI) and in particular, machine learning.

Recommendation 3 calls for improvements in the transparency and use of AI along the lines that have been recommended by the Centre for Data Ethics and Innovation (CDEI) i.e. that it should be mandatory for all public services that impact individuals to declare the use of AI. It also calls for the Equality Human Rights Commission to issue guidance that clarifies how to apply the Equality Act to algorithmic decision-making. Given the breadth and scale of AI use in both the private and public domains, this recommendation is pretty weak.

For one thing, it doesn’t address the use of AI by private sector organisations. This is despite the fact that some of the most egregious abuses are occuring in the private sector with limited means of redress available to individuals. For another, the issuance of guidance on the application of the Equality Act in this regard does not have any impact on the currently low level of enforcement. Suffice it to say that the report’s review of the role of technology on the lived experience of ethnic minorities is very lightweight.

Perhaps this is why the recommendations are only covered in a short chapter within the report, separate from the other recommendations. It certainly gives the impression that the CRED was unsure of what to say about the increasing use of automated decision making, its ability to amplify existing inequalities or how to connect it with the four areas of focus. This is especially worrying at a time when issues of workers’ rights within the context of the growing gig economy are in the spotlight.

Recommendation 23 of the report considers the role of equality data. It calls for the Race Disparity Unit to work with the Office for National Statistics (ONS) and the Office for Statistics Regulation (OSR) to develop a standard way of collecting ethnicity data and for this approach to be pushed across media and public channels. Again, we believe that only the most superficial review of the role of data in aiding our understanding of how inequality plays out in real life could yield such a lightweight recommendation. There is so much that could have been said about data ownership and access in order to support greater agency for those about whom this data is collected.

Whilst both these recommendations are fairly weak (and sit within a report in which 16 of the 24 recommendations are a rehash of recommendations made in other reports), it will be interesting to see whether the government will continue with a pattern set by previous governments and make slow, if any, progress in enacting the report’s recommendations. This is important and has implications for members of marginalised groups that aren’t from ethnic minorities backgrounds. Black and Asian UK residents are over-represented within the British working class and growing underclass. A failure to address issues that impacts them is a failure to address the problems of the working class.

It is clear to many that unregulated data-centric technologies are one of the ways that private sector organisations are reshaping the world of work, the so-called ‘gigification’ of work. One upshot of some of the extreme streamlining practices being adopted in pursuit of ‘efficiency’ is the increased economic precarity of those at the bottom of the economic ladder. This has been a known problem for a number of years now.

In fact in 2016 the government commissioned the Taylor Review to review the UK’s employment landscape and make recommendations on how to improve it. To date the government has failed to introduce changes that have significantly improved the conditions of gig workers. Despite the over-representation of people of colour in this cohort, the Sewell report has no recommendations to make in this regard.

The very serious function, of racism is distraction

In a worst-case scenario, this report has the potential to make our work harder; if it is used to shut down much needed discussion about racial injustice — which our work seeks to highlight and challenge within the spheres of data and technology.

“The function, the very serious function, of racism is distraction. It keeps you from doing your work. It keeps you explaining, over and over again, your reason for being. Somebody says you have no language and you spend twenty years proving that you do. Somebody says your head isn’t shaped properly so you have scientists working on the fact that it is. Somebody says you have no art, so you dredge that up. Somebody says you have no kingdoms, so you dredge that up. None of this is necessary. There will always be one more thing.” Toni Morrison

More optimistically, it will serve as a small distraction that we will do our best to ignore. Instead we will continue to build a coalition of people who want to see data and technology put to uses that support human flourishing. More specifically, we’re building a coalition of impact comprising community organisation leaders, practitioners, technologists and academics who work within Black communities. The roundtables we convened earlier in the year have served as a good source of potential projects. We are now in the process of scoping out and resource planning for these projects.

You can find out more about the work of Data, Tech and Black Communities here: Who we are, what we value, and the change we want to see.

The great distraction- the inCREDible polemic* of a report was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.

Data, Tech, & Black Communities (DTBC) is a community of impact — we work with and for Black communities in the UK to make technology a more positive influence in the lives of Black people. Black people aren’t the only marginalised group in the UK, so interacting with and sharing knowledge with other marginalised groups is core to our ways of working.

Join us as we share what we value, how we work, and most importantly, why you should care.

Better together

We believe the resources we create and share as part of this project can be valuable to other marginalised communities in the UK. So we actively encourage anyone working with a marginalised community to reuse our methods, tools, lessons learned.

We will support work that reduces the harmful (and sometimes unintended) consequences of emerging technologies like Artificial Intelligence (AI) and practices such as extensive use of personal data. In addition, we encourage work that actively explores building life-enhancing technology in the first place.

Who are ‘we’ exactly?

Our community of impact is a group that works towards our shared cause — to make technology a positive impact on UK Black lives. When we use the terms technology or tech, we also include data, and our focus is on data-centric digital technology. Working together, we are learning how tech can support efforts to create a lasting, positive impact for Black communities and other communities often described as ‘marginalised’.

What’s a marginalised community?

Marginalised communities are excluded from mainstream life, which means they don’t have equal access to the same social, economic, educational, or cultural advantages. A community might be marginalised due to one or more of these factors: their race, how they identify their gender, who they love, their age, physical ability, their language, if they’ve been to prison, are working class, or if they are immigrants. In addition, people from marginalised communities can identify as white, Asian, disabled, LGBT+ or a number of other identities.

Black people are diverse; we are disabled, LGBT+, working-class, and many other identities. So we have a common cause with members of these communities, and all of us would benefit from better uses of data and technology. Marginalisation leads to unequal power relationships between social groups, and data and technology can make this worse. That’s why we believe that we work better together.

Our community evolved out of a project funded by The National Lottery Community Fund. Our community stewards are currently three Black women working in technology: Tracey Gyateng, Ade Adewunmi, and Dr Mwenza Blell. Our role is as caretakers of the community until we’re no longer needed. In the formation of DTBC, Edafe Onerhime was a founding steward and has been instrumental in developing DTBC.

Now you know a little more about who we are, what do we hope this community will achieve? What do we value? How can you get involved? For more on that, read on.

What do we want to achieve?

We started with the simple question: How can technology and data be designed with empathy and justice?

The most effective way of assessing how just and empathetic data and digital technologies are, is to ask the question: Do they serve marginalised communities well, or do they reinforce existing inequalities? Unfortunately, marginalised groups are often the proverbial canaries in the coal mine — we feel the negative impact of data and technology in our communities first and hardest. That’s why understanding how data-centric technologies impact us is crucial for the wellbeing of wider society.

In focusing on the impact of data and tech on Black communities what did we hope to find? Insights to help us understand how to build back better and fairer for everyone. We aimed to do this in a way that supports people rather than simply extracts their time, knowledge, and skills while leaving them out of the implementation process. This approach informs all our work.

What’s the problem we’re trying to solve?

Many data and technology organisations essentially test their products on an unsuspecting public — that’s you and me. This is often described as the ‘Move Fast and Break Things’ approach. In doing so, they transfer the burden of testing and validating the merits and safety of their products onto us.

The side-effects of products built in this way often fall first and hardest on marginalised groups. Sometimes these harms are obvious and direct, but often they are broad and diffuse.

These corporations’ approach has several implications:

1. The speed of tech product development means there’s insufficient time for piloting, evaluation, and public engagement. As a result, the potential negative impacts of the products and services aren’t identified and can’t be proactively addressed

2. The harms can take time to appear and can pop up in unexpected places, so it takes a while for people who are affected to join the dots, and organise to bring about change. And even when they do, corporations with a vested interest in staying the same have the funds to lobby governments and promote stories that convince people that the problems aren’t so bad or aren’t down to their products. So, for example, it took years to link the emergence of Airbnb and housing shortages in cities like Barcelona and Edinburgh.

What’s our ultimate goal?

In a nutshell, we want to see uses of data and technology that support equity and human flourishing. That means creating more just and fair technology. It also means identifying and redesigning or discontinuing data-collection practices and data-centric technologies that don’t do this.

That’s why we’re looking at data-collection practices and data-centric, digital technologies that cause problems for Black communities, which might otherwise fly under the radar. We know that keeping track of these technologies and addressing them is an uphill battle for civil society and a particular nightmare for groups already fighting social injustice.

This is the perspective that shapes our core goals. We support people to do this vital tracking and mapping work, and together, we can address the emergent issues. Often when grappling with the fallout from bad tech, we feel it on an individual level. However, we believe that this can’t be fixed on that level; we’ll get much further by organising and taking action together.

Where do we start? We believe we start with the organisations already serving Black communities. Why? Because they already have connections to people in their communities and the organisational infrastructure to be effective.

So what do we bring? First, we recognise that this work isn’t the primary mission of most community or grassroots organisations. As a result, many community organisers and leaders who are already working hard to improve the lives of those they serve don’t have the time, resources, or technical know-how to engage with the issues caused by data and tech confidently.

It’s not just a case of capacity and confidence; it’s also about access. Community organisers and leaders may lack access to and the ability to influence ‘Big Tech’ or the institutions that embed these data-centric products and services in our lives. They may lack the contacts and context they need to make meaningful and positive changes.

Ultimately, our goals are to:

1. Help leaders identify links between the current uses of data and tech and, where they exist, negative impacts within their communities.
2. Support leaders to meaningfully increase the positive impacts of data and digital technologies and reduce the harms.

How will we achieve this?

Beyond our rationale and goals, what are our values? How will we ensure we don’t end up exploiting, extracting, and harming the very people we aim to support? We began by crafting principles that shape our approach and objectives. We wrote these down so our stakeholders knew what to expect from us.

What are our principles?

Our experience as technologists and feedback from the organisation leaders, technologists and academics whose opinions we sought, helped us identify and refine six principles that we felt should guide our work.

We were also inspired by the principles of the Design Justice Network, which proposes an approach that “rethinks design processes, centres people who are normally marginalized by design, and uses collaborative, creative practices to address the deepest challenges our communities face.”

We have six deeply held and actively practices principles:

1. First, we work in open ways. In practice, this means sharing our work and ideas. It also means being transparent about our working methods and welcoming to our methods being reused and repurposed.
2. We recognise that we do not hold all the answers; we are humble and constantly learning from others.
3. We are respectful of people’s time. This means reading up on their prior work (rather than asking them to re-state it for our benefit) and compensating them for their time as appropriate.
4. We aim for our work and events to be accessible to all, making adjustments when we need to.
5. We include a mix of people, from people on the frontline to academics and technologists, and focusing on issues that directly impact Black lives. We meet at times that would work for people.
6. We make the data and tech as relevant as we can to the lived experience of all the people we work with.

What’s our approach?

Principles alone won’t make action happen. So we translated our values into four essential steps that we apply to everything we do.

We began by creating safe spaces for people to share, learn and set our community’s agenda. Next, we worked hard to remove as many of the socioeconomic barriers to engaged individuals taking part in this effort, as we possibly could.

We built on existing work and existing relationships while reducing the potential for the negative impact of hypervisibility for those taking part. Marginalised people are disproportionately at risk of threats, abuse, and retaliation for organising. We worked hard to be thoughtful and keep everyone who got involved safe.

And our objectives?

There’s a lot of work to be done to fulfil our vision “to make technology a positive impact on UK Black lives”. So what can we do within this space? Working with Black communities, we want to:

1. Engage: Actively engage with Black communities through Black leaders- mentoring organisations acting as intermediaries.
2. Inform: Support Black leaders to more effectively recognise how Big Tech’s products and services currently affect, and could further affect, the lives of UK residents.
3. Connect: Develop and enhance relationships between Black leaders and technologists and academics who are working on or studying the impact of the data and tech industry.

What did we assume?

We started this community with some assumptions. We want to share the key ones with you. Challenge us if you think they need to change.

We assumed:

1. The leaders of organisations working formally and informally in Black communities don’t have the time, capacity and set of skills needed to do the work of fully mapping the harms and benefits of data and technology to impacts while doing their core work.
2. Big Tech does not work in isolation; it depends on a network of organisations and institutions, schools, employers, businesses to build an ecosystem that enables its products and services to function at scale. It is this ecosystem that embeds data and technology in our everyday lives.

Any work we do must focus on the entire ecosystem. You can’t address the challenges of data and technology by only looking at Big Tech.

The bigger picture we began to reveal after our initial discovery highlights our assumptions: Scrutiny required! Making technology a positive impact on UK Black lives.

This image doesn’t convey all our discoveries or realisations and there are definitely things missing. However, we do think it works well as as a rough guide to where our heads are at as we move from our initial discovery work to our first project. The image outlines what the big change we want to see — data-centric, digital technologies that enhance Black lives — might look like in practice. For example:

• we think it would like data about people being available to them so they can use it to improve their communities.
• giving people this degree of agency is also why we believe that conversations about data and tech must be centred in real life. We think it’s easier for people to push for positive change if they understand how technology’s positive and negative impacts play out in real life.
• we want to see tech and data evolve to help us engage in our communities and outside our communities safely and effectively.
• we want to see a public that’s informed about and shapes ‘Big Tech’,
• Lastly, we want equitable and effective regulation of data and tech that gives people the levers to make these changes.

We believe that these things, however desirable, can’t and won’t just happen. There needs to be sustained resourcing of the organisers and community groups that are facilitating this work on the ground. That means leaders in this space need access to money, networks, relevant tech, and the internet.

As we build and shape our community of impact together, we’ll test these assumptions and use what we learn to fill in more blanks.

What can you do now?

Thank you for coming on this journey with us. Here are three things you can do right now:

1. Read our very first article: ‘Scrutiny required! Making technology a positive impact on UK Black lives’ where we kicked off this work. It will take around 5 minutes.
2. Get in touch: Contact us about this project, let us know what you think, who else we should speaking to, and how we can make this a success.
3. Share what you know: Contribute to our public bookmarks — ‘Data, Tech & Black Communities’. We’d love your resources, examples of data and technology’s impact on Black lives, or links to other work in this area. If you need help using our bookmarks, read ‘How to use our resources’.

Acknowledgements:

1. Illustrations from BlackIllustrations.com — Life is Good — Lifestyle Pack, New Year, New Vibes Illustration Pack, and The Disability Pack.
2. Photo by uncoveredlens from Pexels and modified in Canva.
3. We’ve licensed diagrams we created under an Attribution 4.0 International (CC BY 4.0) license.

Who we are, what we value, and the change we want to see was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.

The purpose of this blog was to provide a primer to participants attending a roundtable on the use of data and technology. It explains key terms used and provides relevant examples of how data and technology can negatively impact upon Black lives in the UK. Many of the points we raise apply to other marginalised communities too.

What are data and technology?

The terms data and technology are understood in a multitude of ways. A common definition of data is: a collection of curated information that can take the form of numbers, words, pictures. An example is the Companies House register — a database of registered UK companies containing details such as directors’ names and service addresses, company accounts, charges and other types of business activity. This register (which isn’t without its issues) is maintained by the government agency, Companies House.

For our purposes, we can broadly think of technology as the various digital tools and systems (sometimes further enabled by data and machine learning) that deliver or support specific tasks. Video conferencing tools like Zoom which are facilitating remote working, are a good example. Another example is the use of Artificial-Intelligence (AI) enabled software for everything from recruitment to employee ‘productivity’ tracking and staff retention.

How are data and technology used with regards to employment and enterprise?

Data and technology are undoubtedly useful aids for the smooth management of many activities related to employment and business. For example, they can help with supply chain management or to discover trends that can help businesses and government policy. However, the use of machine learning (which is powered by data and digital tech) can exacerbate existing discriminatory trends. Human biases- which are found in all datasets- are picked up by the algorithms as trends; these patterns shape future predictions. For example, when Amazon’s hiring algorithm ‘learned’ to associate successful hires with certain gender-correlated variables, it downgraded CVs from women. In other words, it reflected and sought to replicate the company’s past hiring patterns. Amazon discarded the algorithm.

However, other large multinational employers, like Unilever, are still using AI-enabled software to screen job applicants. The Information Commissioners Office, has rightly sounded the alarm about the privacy, bias and discrimination risks. But algorithms are affecting other aspects of work too. For example, UK Amazon warehouse workers have complained about the relentless pace (driven by algorithmic decision-making) at which they are required to work. So have their US-based counterparts.

Elsewhere, large ride-hailing platforms such as Uber or food delivery platforms such as Just Eat or Deliveroo are also affecting the world of work. These organizations exert a growing influence on the lives of ‘gig workers’ as well as small business owners such as restaurateurs. Black people are twice as likely as their white counterparts to be on zero-hour contracts so Black workers are disproportionately impacted by this influence. This has knock-on effects. For example, precarious employment has been hypothesized as one of the reasons Black and other ethnic minority communities have been so disproportionately affected by Covid-19.

This is one of the reasons why it’s so important that we have access to data that will help us understand the impact of these technologies. It would also help us figure out what needs to be done to address it. Unfortunately, this data isn’t always collected as comprehensively as it needs to be; in other cases, data is collected in opaque ways and put to malicious and punitive uses e.g. DWP’s surveillance and data gathering about people receiving benefits.

We know that the plural of anecdote isn’t data but we are collating resources e.g. reports and articles, which capture the impact of data and technology on Black workers and business owners in the UK, here. Do let us know of other resources we can add to this open library.

What should we do?

The round table is not going to be the forum for solving the issue of greater scrutiny and accountability around the use of data and technology within employment and enterprise settings in the UK. But we will explore:

• Our collective understanding and concerns about how data and technology are used when it comes to employment and enterprise and how these things play out in our own lives and some of the communities we are part of;
• Ways we can stay informed about how data and technology are being developed and deployed in these areas;
• What a network capable of holding institutions and organisations to account for their use of data and technology, could look like?
• Who else should be involved in this discussion?

Key Terms

Data: curated information such as numbers, words, pictures collected for a specific purpose.

Technology: digital tools and systems (sometimes further enabled by data and machine learning) capable of delivering or supporting specific tasks.

Algorithms: a list of rules to follow in order to solve a problem. (See this great presentation from Edafe: What do we mean by an algorithm?).

Artificial Intelligence (AI): A catch-all phrase that can be misleadingly used. It covers everything from the aspirational (and currently unachieved) idea of a machine capable of general intelligence and true learning e.g. RoboCop — through to what is often called narrow AI where machines are very good at a single task, like playing chess.

Machine Learning: a branch of (narrow) AI where computer algorithms discover patterns within data and use them to build models that applies to new data that it is presented with. Accessible guide here.

Data Science: A subject/field of practice that transforms data into information, most commonly, by using machine learning techniques. Accessible guide here.

We ran roundtables in March 2021 connecting UK Black communities around Data, Tech &: Education, Crime & Justice, Employment & Enterprise, Health, and a final presentation.

View our presentation here: Roundtable Presentation.

Data, Tech & Black Communities is a project funded by The National Lottery Community Fund. We respect your privacy.

Data and Tech Use Within Employment & Enterprise Settings — A Primer was originally published in Data, Tech & Black Communities on Medium, where people are continuing the conversation by highlighting and responding to this story.