How can we facilitate the research and industry to help the field of artificial intelligence flourish in the Nordics?

Is there an advantage to thinking regional when it comes to artificial intelligence? After all, there are several companies and research environments that already collaborate. Yet I would ask: where is constant collaboration in the Nordics on artificial intelligence policy? Does it exist?

There is the Nordic council of ministers.

Digitalisation

Within this there is collaboration that goes over time, albeit perhaps a more general approach.

This has an even broader focus regionally as well, on the Nordic and Baltic countries.

The Nordics or Nordic countries are a geographical and cultural region in Northern Europe and the North Atlantic, where they are most commonly known as Norden (literally “the North”).

This term includes:

• Denmark.
• Finland.
• Iceland.
• Norway.
• Sweden.
• Greenland.
• Faroe Islands (part of the Kingdom of Denmark).
• Åland Islands (Finland).
• Jan Mayen and the archipelago of Svalbard that belong to Norway.

If we extend to Nordic-Baltic then the group of countries presently referred to by the shorthand Baltic states: Estonia, Latvia, and Lithuania.

Will this be cohesive?

I mean: in practice could one conceive that these could work together.

Where do you find the people willing to work in a steady manner — shaping collaborative relationships across the region?

The field of artificial intelligence, despite its often displayed varieties of applications, is still a field of research.

As such if there was an expectation for this to work an AI policy group would need to hear and understand what these communities needed. It would need to help facilitate funding over longer periods of time.

Additionally it would be important to collaborate on purpose and direction.

Yes, foundational research is important and existing industries will likely want to be involved to shape this.

On the other hand: what is important now for society at large?

There are a set of different crises that are experienced across the world.

• A massive drop in employment – labour issues.
• The climate crisis — reducing emissions.
• A rapidly spreading pandemic.
• Microplastic content increasing and spreading in the oceans.
• An economic crisis, an overall system of comparative GDP measurement that does not work and hinders the right focus.

Artificial intelligence cannot solve these problems.

However, can it contribute to solutions or can the wrong applications worsen the situation? That is a more important question.

So… How could a group look like?

There are already departments in governments working on technology policy.

The Norwegian department is called ITPOL, however there are several sections spread out across the government working on digital applications.

Alternatively, one could think of it as a progressive, smaller group working together.

However what makeup would this group have?

What kind of people would be involved?

What would the activities be?

How could they make a difference for society through being of help in discussions with researchers and industry?

There is a Nordic AI Artificial Intelligence Institute.

Nordic AI Artificial Intelligence Institute

It seems they are focused more on consulting.

There is Nordic.ai

Nordic.Ai

We have Nordic Five Tech network:

Nordic network for artificial intelligense

Then there is Claire AI that has Nordic representation:

CLAIRE

So there is plenty to think about.

Already several collaborations ongoing.

Yet no clear collaboration on AI policy.

Most of the funding in Norway is according to the AI strategy in Norway private.

Why not collaborate in the Nordics?

The region is already cohesive and there are several initiatives for collaboration that already exist.

With this I hope I can spark a debate or interest — questions.

Who will facilitate collaboration on artificial intelligence policy in the Nordics?

This is #500daysofAI and you are reading article 346. I am writing one new article about or related to artificial intelligence every day for 500 days. My focus for day 300–400 is about AI, hardware and the climate crisis.

A Collaboration for Nordic AI Policy was originally published in AI Social Research on Medium, where people are continuing the conversation by highlighting and responding to this story.

There is a Growing Engagement to Consider AI in the Context of Climate Change

It seems more initiatives are starting on climate change and artificial intelligence of late. Most of these initiatives have an applied scientific focus, rather than attempt to combine different types of field to address the urgent issues within the infrastructure that enables AI, however that may be a different story, and there seems to be initiatives on energy relating to AI. I want to focus on two initiatives, an AI track at Stanford and a new centre at the University of Cambridge.

AI for Climate Change Track at Stanford University

A Bootcamp for Cutting-Edge Research at the Intersection of AI and Climate Change

“The AI for Climate Change Bootcamp will convene teams of Stanford students this Winter quarter to build AI models for addressing key problems in climate change.”

The bootcamp will be led by the Stanford Machine Learning group, under Professor Andrew Ng alongside different communities at Stanford University.

• PhD students in Professor Andrew Ng’s lab
• Faculty of the Department of Earth System Science
• The Department of Civil and Environmental Engineering
• Scientists from Descartes Labs

The Bootcamp starts Jan 10th, 2020. There is a requirement to have completed several computer science courses prior to joining the course.

You can find the bootcamp here:

AI For Climate Change Bootcamp

AI for the study of Environmental Risks (AI4ER)

“The UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) will, through several multi disciplinary cohorts, train researchers uniquely equipped to develop and apply leading edge computational approaches to address critical global environmental challenges by exploiting vast, diverse and often currently untapped environmental data sets.”

There is a split focus within two areas:

1. Environmental data classification, integration & analysis
2. Environmental modelling

It is a one-year Master of Research (MRes) course with a taught component and a major research element, followed by a three-year PhD research project.

This centre is also focused on the natural sciences, this are the type of candidates they are looking for:

• natural sciences (e.g. physics, chemistry, earth sciences, biology)
• engineering
• computer science
• mathematics

You can find more information here.

AI for the study of Environmental Risks (AI4ER)

Short Note on Social Science vs. Natural Science

Since I study computer science as well I do understand that there is a very practical aspect to this type of education that requires an understanding of these conceptual frameworks. There has to be a degree of experience within these different fields to understand math, programming etc.

I do on the other hand wish that they opted in to create a space for a different type of participant who had another focus area. It seems if you are a social scientist wanting to combine these studies a heavier focus on the natural sciences is needed before it is possible to access these types of courses.

I have so far not seen any course focused on AI within social science, although there are plenty of Science and Technology studies wherein you could certainly have a research focus on artificial intelligence.

This is #500daysofAI and you are reading article 171. I write one new article about or related to artificial intelligence every day for 500 days.

Research Initiatives for AI and the Climate Crisis was originally published in AI Social Research on Medium, where people are continuing the conversation by highlighting and responding to this story.

Talking About Artificial Intelligence and Climate Change

For today I started writing an outline for a talk and I thought I would share it with you. I am not sure it is great or decent yet however I would love your opinion on it. It is not my intention to be overly moralising, simply to raise a few questions.

Outline of a Talk — Fifty Shades of AI

My talk is named Fifty Shades of AI.
Beyond making AI work.
When the algorithms run.
When the machine learning techniques are learning.
What then?

My name is Alex, I’m 28 and I am currently studying computer science and social science at the University of Oslo. I have had a personal project of writing about AI every day for 500 days and I call this 500 days of AI. I will be on day 100 next week. So I will try to touch briefly on three topics I believe to be of great importance in the present.

1. Beyond operational AI
2. Funding and self-policing of ethical conduct by large technology firms.
3. As well as shifting the focus of the field of artificial intelligence from ease of use or extraction to addressing the climate crisis.

We ask ourselves the question of who an autonomous car should hit. When perhaps we should be asking which vehicles should be made autonomous?

It takes a lot of minerals to produce the graphics processing unit of a car or CPU and that means mining. How much mining activity will fleets of autonomous cars require to be maintained or to be operational?

If autonomous cars are running on the most efficient hardware how much energy does it take to constantly navigate the roads as opposed to the energy saved by more efficient driving?

Jeff Hammerbacher a prolific developer in his 20s said:
"The best minds of my generation are thinking about how to make people click ads. That sucks."

I do want to believe technology companies. I want to believe ‘don’t be evil’ as Google says; I want to believe in bringing the world closer together; I want to believe in being empowered to achieve more.

I want these companies to be ethical, to do the right thing in the right way for humanity in these times of a climate crisis. However we have seen examples before of self-policing ethics, corporate science. The tobacco industry funding scientific health research; the pharmaceutical industry regulating medical research; oil companies looking after their own oil spills; mining companies monitoring rivers or surrounding lands. The last thing we need is self-policing technology firms.

Facebook funds ethics institutes.
Google owned DeepMind builds evaluation tools for AI algorithm performance (bsuite, Google it). Is this somewhat problematic?

If at times natural science is so artificial and social science is not very socially responsible where does it leave us?

Saving the Amazon perhaps, not the company shipping products very quickly, no the Amazon forest nurturing life on earth.

Saving lives in hospitals with cognitive machine learning techniques while protecting privacy.

Thinking seriously and intently on data citizenship. Then making regulations and initiatives to ensure a higher degree of fairness.

To think that algorithms can show behavioural patterns and set out to change these and make them transparent.

Building new technology has been an interdisciplinary endeavour and it still is. Yet we need to go from thinking users to thinking people, citizens. Together we can work to bring both intelligence and understanding closer.

The fields of social science and computer science can work together and this collaboration will be important if we are to contribute in the time ahead. A time of crisis.

Technology cannot save the world, but it can assist in the process of making the world a bit better. You can make the world a bit better.

This is day 94 of #500daysofAI. My current focus for day 50–100 is on AI Safety. If you enjoy this please give me a response as I do want to improve my writing or discover new research, companies and projects.

In Between Social Science and Computer Science was originally published in AI Social Research on Medium, where people are continuing the conversation by highlighting and responding to this story.

Merging Opinions and Questioning Development

For today I thought I would jot down some rapid thoughts on the topic of merging opinions in the context of planning. It will likely be eclectic and jump between different points within the area.

Reaching an agreement

The point to a participatory process seems often a teleological goal, particularly in building or planning processes which I am quite concerned with these days. Should it be built in the first place? Why here and now seems pertinent with the larger planning regulations. There are different levels where planning is being undertaken, and today I attempted to look into the different levels locally in Norway.

There is (1) a national document outlining expectations, (2) a regional planning strategy; (3) communal and planning program; (4) regulation plan; (5) building application. There are rather a few steps going between these different instances and usually it ends up into a process in the real world.

Reaching an agreement does not seem to stop by all of these steps of course, and it is mentioned that power players do work locally as politician sometimes on behalf of citizens or property developers to facilitate within or outside of the flexible frameworks that at time makes up the planning and building laws. The conflicts seems to arise on the regulation level between the building application and local regulation.

As is shown by this figure (in Norwegian) there is a possibility for the builders to somewhat ignore the regulation plan. When a concrete plan becomes more clear there is a higher likelihood that people will disagree with what has been planned built. Maarit Kahila-Tani in Reshaping the planning process using local experiences attempts to draw upon existing research from Brown and lays it out in an interesting matter, dividing the collective and individual.

In her writing she seeks to develop a framework for participatory planning support system. As such systems to assist in the process of planning.

In her writing she mentions Maptionnaire a few times, a map based survey. The idea of these planners is to attempt to get an understanding of what people think of their environment.

Digging into their different approaches I can see a wish to move towards a platform that has a lot of technology integrated. However there seems to be a lack of expertise or funding — as such they end up with websites that seem a lot like reports that previously have been produced (slightly more clickable). A wish seems to be to get a grip of larger amounts of data to produce planning insight and ensure a form of inclusion or democratic participation in local political decisions.

Let us for a second or two imagine that they had access to tools that could ‘align’ opinions. It would be fascinating to think about all the potential issues that could arise in a small neighbourhood alone. Then again the current processes cannot take a larger set of opinions into considerations (or I assume so), although people talk there is a cliché of white-haired men at planning meetings (allmøte) which sometimes is the only mandatory activity property developers sometimes have to perform.

If there was an amalgamation of opinions what would it look like?

If machine learning focused on text was used to gather and synthesise opinions in certain categories that could be possible. We could imagine a combination of text analysis as well as chat bots to generate different type of inputs. If artificial intelligence, so to speak, was used — would it be any improvement at all?

Sentiment analysis seems a course of action in this regard. It is the process of computationally identifying and categorising opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral. If you could do such a thing then you could generate a heat-map of the dismay in a given area.

Then again this heatmap may be a reproduction of the same interest only intensified (of the old white-haired men), then again if it was a digital tool you could dare to think that other groups may get involved. It is currently possible for me to check my local plans in the public records online. As such I can search for my street and look at all the plans that are being discussed in my local area by the government. This can be said to be an unparalleled degree of government transparency and the Norwegian government must be applauded for this effort.

There seems to be possibilities here for getting an easier overview of your area. I have seen tools being developed by companies in Norway that creates a map easy to look at, although again — Norway is already using these type of frameworks or tools freely available to citizens. I can access regulation plans on the Internet to discover what type of regulations that can be seen in my area if I am particularly curious or worried.

So back to gathering opinions or reaching an agreement. It seems highly possible to integrate map-based questionnaire tools for the Norwegian government, however the question is if they should have a large in-house team doing this or invest in Maptionnaire with close follow or the Norwegian company Spacemaker AI for that sake. I heard that Spacemaker AI had moved towards collaborating with the Norwegian state already (rumour of course), as such we may perhaps expect movements in this direction in the near future. Another interesting case is the city bicycles in Oslo and the open data.

Can we say that movements is opinions or is that too passive? An opinion is a view or judgement formed about something, not necessarily based on fact or knowledge. It can be a generally held view or made by an expert. In this regard who is an expert? In the new project aforementioned (Folketråkk) people are categorised (common practice in user experience or service design) into citizen, municipality, architect, property developer and politician. These seem the obvious users from a design agency or government standpoint. Citizen is a broad category, are they not all citizens, or do they have to lack power to be citizens — not involved in the process perhaps?

It is not easy to understand first if we categorise and then if we analyse what to gain. If it is a kindergarten or a shopping mall a lot of opinions seem made prior to any type of participatory arrangement. I have adapted a model from the Green Building Council that shows project phases, the curve for dialogue and cost associated with change.

If building projects have participatory elements as required per building regulations or voluntarily initiated it seems most are located towards the later stages of the project.

1. How early can we detect what is happening in our neighbourhood and have an opinion about it?
2. If we have an opinion how is it categorised, quantified or understood?
3. If there are too many opinions can we use sentiment analysis to map a certain area based on different approaches?
4. How can that be complemented with qualitative insight?
5. If the process of participation has worked perfectly and something has been suggested to change — accepted by the developer, how late in the project was this done?

When opinions are formed it certainly raises questions of the probability of having them recognised. Opinion derives after all from Old French opinion “opinion, view, judgements founded upon probabilities” as such we are attempting to calculate based on certain heuristics. People working in environments with code or programming often operate on heuristics or opinions that are reflected in how these solutions are made or implemented.

That is certainly worth taking into consideration when a solution is being built,

– but that is of course simply my opinion.

This is day 90 of #500daysofAI. My current focus for day 50–100 is on AI Safety. If you enjoy this please give me a response as I do want to improve my writing or discover new research, companies and projects.

Artificial Intelligence in Participatory Planning was originally published in AI Social Research on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI Safety ratings — discuss!

Safety in the field of artificial intelligence is a challenge to discuss. When I have been in discussion with friends or seen business conferences and events so far there has been little talk of specifics in AI Safety. Perhaps I am in the wrong communities. However to me it seems like very few if any consider implications in AI Safety. If by any chance I meet someone they usually approach it from a technical, engineering or economic perspective. What other areas remain? Well for one what: what is important in AI Safety?

What is important in AI Safety?

What do we want to keep safe or what do we want to protect? We could talk of the field of AI in broad terms or criticism against autonomous vehicles or WalMart’s use of machine learning techniques on checkout. Is it as such humanity or humanness we want to keep safe? Is it investments, financial analysis or insurance?

Securitization as a financial practice (not in international relations) is the pooling of various types of contractual debt such as residential mortgages, commercial mortgages, auto loans or credit card debt obligations. These can be lumped together into bonds.

As an idea intended to keep investors safe from failure of individuals to repay loans, it can be said that securing loans contributed to the financial crisis in 2008 (aka subprime crisis). Who decides how secure artificial intelligence is? Infamous agencies rated loans as triple A (AAA), there is talk of monitoring algorithms and even in some cases certifying.

Triple-A bonds, or AAA bonds, are those considered the absolute safest by bond rating agencies, yet who decides the safest algorithms. Private companies, states, and NGOs do at times invest in these solutions yet there is little assurance of quality or safety. Who or what entity keeps it safe?

OpenAI and Neutrality

The most promising organisation working to make AI safer was OpenAI until it became privatized (despite being owned by a nonprofit). If any could be said to have the beginning of a capability and a role it could be OpenAI. It was after all created to make artificial general intelligence (AGI) safer or at least to ensure it benefits humanity.

“OpenAI’s stated mission is to ensure that all of humanity benefits from any future AI that’s capable of outperforming “humans at most economically valuable work.” Such technology, dubbed artificial general intelligence, or AGI, does not seem close, but OpenAI says it and others are making progress.” Wired 2019

Of course even seemingly independent actors are not neutral and any rating agency if it were to be a nonprofit would not in any way be guaranteed to take neutral decisions. It is additionally a question of whether setting laws in an international context would help if they could not be enforced in certain cases (humanitarian law as an example).

Points for discussion

• Who could be an actor taking responsibility in rating AI?
• Should this be done at all?
• How could it be done if it was done?
• Think of your own question and post it in the discussion.

This day 75 of #500daysofAI writing every day about artificial intelligence.

How Safe is a Solution Within the Field of Artificial Intelligence? was originally published in AI Social Research on Medium, where people are continuing the conversation by highlighting and responding to this story.

For Safety, Society and Science?

Yes I Study Social Science

I would like to begin the article by clearing the air. I thought I would get that off my chest too, so you understand the position from which this article is written. I am a student at the Faculty of Social Science at the University of Oslo. My subject background is however a mix of anthropology, ethics, machine learning, management and political science. A mixed bag so to speak.

This short text is inspired by an article affiliated with OpenAI also published in the journal Distill. In this connection let me explain both of these entities before proceeding to some thoughts in regards to the article.

OpenAI’s mission is to ensure that artificial general intelligence benefits all of humanity. They are a team of a hundred people based in San Francisco, California. The OpenAI Charter describes the principles that guide them as they execute on their mission. We may examine this charter closer at a later time perhaps in another post.

Distill is a modern medium for presenting research. It is a journal for research on machine learning. It argues that the web is a powerful medium to share new ways of thinking. In their opinion research should be clear, dynamic and vivid. It is devoted to explanations native to the web as opposed to the typical pdf. format in science.

AI Safety Needs Social Scientists

The paper by the same name which this title discusses our perceptions on AI safety. It came to my attention when it was posted on the OpenAI blog earlier this year in February. You can read the full paper if you click here and it is written by Geoffrey Irving and Amanda Askell.

Geoffrey previously worked at Google Brain and now works with AI Safety at OpenAI with a PhD background from Stanford. Amanda works with ethics and policy at OpenAI and has a PhD in Philosophy at New York University as well as a BPhil from Oxford.

All quotes in this text will be from the aforementioned paper.

Their writing argues that the AI Safety community needs social scientists to tackle a major source of uncertainty about AI alignment algorithms: will humans give good answers to questions? The simple answer is to include social science in the development of machine learning (ML) and in most cases prototype with human debaters before implementing ML.

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Importance.

Properly aligning advanced AI systems with human values will require resolving many uncertainties related to the psychology of human rationality, emotion, and biases. These can only be resolved empirically through experimentation — if we want to train AI to do what humans want, we need to study humans.

• Their proposed solution is to replace machine learning with people, at least until ML systems can participate in the complexity of debates we are interested in. Try with human participants first before considering to replace with ML
• Think about how alignment algorithms will work once we advance to tasks beyond the abilities of current machine learning

1. For the specific example of debate, we start with debates with two ML debaters and a human judge
2. Then switch to two human debaters and a human judge
3. The result is a pure human experiment, motivated by machine learning but available to anyone with a solid background in experimental social science

Lastly they encourage collaboration so be sure to contact them or read up on the different sources they mention in their paper.

There are many institutions engaged with safety work using reward learning, including our own institution OpenAI, DeepMind, and Berkeley’s CHAI. The AI safety organization Ought is already exploring similar questions, asking how iterated amplification behaves with humans.

Disclaimer: the paper AI Safety Needs Social Scientists focuses on the human debater and it is far more complex than presented in this short article. This short text is reductive and only aimed at sparking your interest in the topic.

Afterthought On Further Risks

It is curious not to see different subject areas within the social sciences more involved in the question regarding technology. As mentioned in my last article on Scandinavian AI Strategies in 2019 the Swedish strategy for AI contains thoughts regarding sustainability. If the application within the field of AI is for society, which I presumes it is, and for the ecological sustained habitable planet we are on — then the field of AI must be far more inclusive.

As an example social science is not mentioned with a word in the AI Sector Deal by the United Kingdom. It is not mentioned either in the Swedish National AI Strategy that had a stronger focus on sustainability.

The paper I have looked at in this article does not talk much about how we can change this focus rather it talks closer in regards to methods in training AI through machine learning techniques foreshadowed by human decision-making. It does not talk of which different fields within social science beyond philosophy that could be important to the development of AI.

The paper also creates an unfortunate dichotomy or oppositional pair between science and engineering:

Most social science seeks to understand humans “in the wild”: results that generalize to people going about their everyday lives. With limited control over these lives, differences between laboratory and real life are bad from the scientific perspective. In contrast, AI alignment seeks to extract the best version of what humans want: our goal is engineering rather than science, and we have more freedom to intervene.

Why is this an unfortunate oppositional pair? Is not engineering a science and can science be engineered? This perspective ignores a large field of research within critical views within Science and Technology Studies (STS) which is certainly a field of its own. Yet I am inexperienced so perhaps I am wrong.

Conclusion

1. We need a closer consideration of social sciences in the field of AI.
2. Investments in AI Safety needs to be in a diverse range of social sciences and collaboration between fields. It will truly be the challenge of our lifetime to ensure appropriate applications within the field of AI.
3. Viewing science as opposed to engineering may be dangerous for AI Safety. These views and their American-English roots should be questioned perhaps by scholars in Science and Technology Studies.
4. If sustainability or social challenges are stated as important in AI strategies yet no investments are made to follow there must surely be room for improvement in this regard.
5. If left unchecked this could cause large issues for humanity going forward. This is clearly a severe lacking aspect in current AI Strategies.

So with that day nine of #500daysofAI is over and out.

Apologies for my strict wording in this last segment, however I wanted to speak frankly to you reading this article. I would be overjoyed to hear your opinion in the comment section if you agree or disagree.

–

What is #500daysofAI?

I am challenging myself to write and think about the topic of artificial intelligence for the next 500 days with the #500daysofAI. It is a challenge I invented to keep myself thinking of this topic and share my thoughts.

This is inspired by the film 500 Days of Summer where the main character tries to figure out where a love affair went sour, and in doing so, rediscovers his true passions in life.

I hope you stick with me for this journey and tell me what you think!

Social Scientists and AI was originally published in AI Social Research on Medium, where people are continuing the conversation by highlighting and responding to this story.

Artificial Intelligence already influences several aspects of our society. Take a look at consumption habits, election results, interpersonal relations, trade, cities, culture, law, the military, the climate and more — in each field it is possible to see effects of AI either directly or indirectly.

It’s no secret, advanced technology plays a continually greater role in our lives and work. In this puzzle, some pieces could be explored further than has been the case until now, particularly the connection between AI, applied machine learning, other algorithms and social science. So how should we bridge this, and consider the larger effects on society as a whole by viewing these topics from an interdisciplinary perspective?

As of May 2018 there are 7.6 billion people in the world, and in the fourth quarter of the same year, Facebook had 2.3 billion monthly active users. Lately, the 2019 Hootsuite/WeAreSocial digital report came out, telling us that 57% of the world’s population now has internet access as 360 million new people went online for the first time. Something between 1.5–2.5 trillion searches happen on Google yearly, and the world’s digital community is predicted to spend 1.2 billion years in total using the internet in 2019. That means roughly 35–50% of the global population are being influenced by applied machine learning and narrow AI algorithms every month, while simultaneously getting value from usage.

These are quite abstract numbers due to their size, while also being concrete enough to show how larger parts of the global population has access to technology. But this access does in no way necessitate gaining the benefit of these technologies. Especially since they are used for gathering quantitative data used for influence and decision-making often regarding resources or large transnational interests that affect those who use these services. In other words: usage is contrasted by being used.

The mission of narrow AI

“When reading the news, you might see the terms ‘general’ and “narrow” AI. So what do these mean? Narrow AI refers to AI that handles one task. General AI, or Artificial General Intelligence (AGI) refers to a machine that can handle any intellectual task. All the AI methods we use today fall under narrow AI, with general AI being in the realm of science fiction.”
- Elements of AI (www.elementsofai.com)

There is something admirable about organizing the world’s information and making it universally accessible and useful (Google); to give people the power to build community and bring the world closer together (Facebook); or to be earth’s most customer-centric company; to build a place where people can come to find and discover anything they might want to buy online (Amazon). There are many more examples, all of which does provide some form of utility and at times agency.

Thus having more information than ever, being more connected and having easier access to most things sounds great when we say it out loud. Yet if we zoom in on different locations this has not necessarily been the case everywhere. Consumer insights are offered to companies by Google and Facebook or used actively by others such as Amazon. This insight has to be mined, a new form of extraction requiring both physical storage and vast amounts of energy. The best minds of our time are spending their life trying to market products to customers in a better way. Echo chambers of ‘likes’ begetting new targeted ‘likes’ contributing to polarisation; an algorithm can be categorised to a certain extent as racist in its judgement leading to imprisonment; and connections that show social comparison leading to mental health issues. The lofty ideas we had about technology as neutral have passed for some. Following this is an understanding that technology is not objective, not at all.

Technology is social

As such, if we now know that technology is social, a need arises for creating teams that consist of a diverse range of thinkers when working on these issues. Simply mixing in social scientists is additionally not enough, being educated in ethics does in no way guarantee ethical behaviour. Organisations today should work actively in small units across and between disciplines to facilitate a greater understanding of the various aspects that needs to be explored when we implement new technology.

As authors of this article, being engaged and interested, we are just students or recent graduates. Amidst climate change, weaponized artificial intelligence, refugee crises and more rising inequality — when most things seem surreal and truly difficult– it is precisely at this time when we need to gather and think. Think, so that the actions that we take are better, if only slightly better, and do our best to shift the course that we seem to be drifting down.

This is just the start, and there are some possible goals we can consider.

1. One goal may be to increase the collaboration between social thinkers and programmers in research efforts (As suggested by OpenAI).
2. A second goal could be to encourage a deeper understanding of programming for social thinkers and social science for programmers.
3. A third possible goal is creating platforms to gather this type of combined, interdisciplinary research and communicate it to students as well as businesses.
4. A fourth potential goal is taking part in development of solutions to monitor algorithms for possible behaviour that may disadvantage a population or prove dangerous for society. There are likely other possibilities for groups or goals.

Goal zero

Then there is poverty, water, urbanity, deprivation, etc. A list of issues we are facing locally that are now inevitably directly influenced by this technological push. If we were to forget these then all the aforementioned ambitions would be largely irrelevant. Is artificial intelligence a smokescreen in this sense? We tend to rapidly ascend to cloud-conversations of data or deep learning when the effects of these applications have some consequences we need to deeply consider. Technology in itself is not irresponsible. However, in the ‘agile’ environment of speed we find ourselves in, it is easy to forget why we were developing these novel solutions in the first place. It is challenging to propagate slow or ethical programming in this swift environment.

The goal zero could be to make AI work for our interplanetary shared ecology. Because social sciences, humanities, or computer science must not forget the world in which it is situated and depend on.

One incremental change on a platform with 100,000, one million or many billion users can slightly change how all of them act in real life. The indirect effects on society from the change of production processes in industry, lead to questions like: How will AI help optimize processes so that they use less of our natural resources? Can such optimization end up with us overusing another resource in the process? How will it affect our banking systems, our shopping habits, our energy system or our food production? All of these affect society and nature.

There are no policy makers or business leaders to influence first. It starts with you. If we can think of solutions or build platforms that is where it starts. It starts with collaboration between a few people. “Never doubt that a small group of thoughtful, committed citizens can change the world; indeed, it’s the only thing that ever has.” Margaret Mead said this — It is simple, obvious, and rings true today. Although we are influenced, and in the context of a rather large intangible ‘global’ society, it is a few people coming together in a variety of ways that facilitate the local steps staggering forward. We have a responsibility to do everything we can to understand our notions of progress while moving ahead.

AI Social Research is a small contribution, with a purpose of connecting thinkers together to engage in conversation and expression; to provide broader perspectives on AI and its effects on society through researching as an interdisciplinary group from backgrounds in social sciences, fin-tech, engineering, product development, renewable energy, psychology and informatics.

The social implications of AI was originally published in AI Social Research on Medium, where people are continuing the conversation by highlighting and responding to this story.