Goal Bias.

Goal bias can be the very first mis-step in human management. For example, a goal such as “automate the pre-selection criteria of candidates for a job posting” may unwittingly overlook the diamonds in the rough that we would have uncovered via personal interactions, such as a phone screen or in-person interview. Another example: a goal such as measuring teacher efficacy might inaccurately tag a teacher with low-scoring kids in a high-poverty district as ineffective without considering the socioeconomic factors that might also contribute to low test scores. We mitigate goal bias by ensuring the goal is well-formed and well-intentioned along the lines of human pursuit, rather than human management. It’s important to note that in some cases this will run afoul of being efficient and possibly, arguably, profitable. This is not a one-time effort; goal bias requires us to constantly examine the questions we’re asking our data and what outcomes can be derived.

Data Bias.

This one falls in the category of “you don’t know what you don’t know.” However, I find this to be a bit of a cop out, especially when data scientists advocate “letting the math do what math does.” Together, these two penchants can yield catastrophic results such as zero Black/African faces being identified as beautiful in a model designed to determine archetypes of beauty. How does that happen, you ask? Easily. If we train a model with data that is already biased (i.e. the test data does not include Black/African faces, the resulting algorithm will be biased, too). So, if mathematics is our super power, we must demonstrate dominion over data modeling (instead of the other way around).

Feature Bias.

Feature bias offers more cautionary tales of the danger of “letting the math do what math does.” Take for example, credit underwriters Zest Finance, who identified a new risk: people who fill out loan applications in all caps are much higher risk. I want to suppose they also took into account that an entire generation is now devoid of learning in the etiquette and meaning of handwriting past the 3rd grade, but I am sure there was no mitigation of bias on this particular feature (mind you not even one to proclaim it irrelevant.) I am sure some super smart designer used antiquated psychological handwriting analysis to skew bias in the direction of including handwriting as a feature in determining credit worthiness NOT considering that the youngest two adult generations are no longer taught handwriting beyond formation.

At the last conference I attended, someone asked a question about how we should deal with a feature that we know has a greater or lesser impact. The facilitator answered, “Allow the math. . . ” Inherent in this answer is implicit bias that represents and doubles down on systemic and institutional biases. In other words: bias in, bias out. Humans naturally employ bias as a means for survival, but it is when these biases coalesce into a systemic and/or institutional bias that we need to address and mitigate. It will take sincere improvement efforts in systems thinking and observation of societal constructions to ferret out dangerous feature bias. And, when we do discover bias, how will we deal with it?

Model Bias

We all know that not all models tell the story we want to tell, nor do they all yield the answer to the actual question asked. Furthermore, in some cases we do not have a clear line of sight into how the model factors the data. It would be great if these “black box” models could behave responsibly, like humans, but that’s not going to happen. In many regulated industries, such as insurance and finance, this is an issue. And as more and more industries adopt machine learning, we will be asked to explain how and why a model works the way it does, regardless of regulation.

Demystifying the black box, cracking it open and revealing its innards, is one of the biggest challenges we face. And it’s important for two reasons: we want to know how and why that particular model was designed for 1) efficacy and 2) improvement.

While I applaud these efforts, I would like to see us put more emphasis on avoiding the bias in the first place. That’s a tough sell because at this stage in the game, we incentivize speed to market over quality of outcomes. Instead, I’d like to see an incentivization plan that allows us to profit from the positive impact of the models we produce. But for now, we must consider the quality of the outcome to the best of our ability within the time constraints we’re given.

So, we ask ourselves: does the model include variables with explicit or implicit bias? Are we extending a self-informing feedback loop that exacerbates a systemic or institutional bias? And, if so, how are we mitigating those biases. Are we creating opposing models? For example, if we are using data to model criminality, are we also modeling for policing on behalf of the citizenry? Are we even testing for that? Do we know HOW to test for that?

I’ve given us a lot to think about. We, the data geeks and Air Benders, will shape the societal norms of tomorrow. I believe we have a responsibility to “bend” the data to facilitate innovation that will foster societal good. We are not modeling for the masses; we are the masses who are modeling our future.

Vida Williams is the Data & Machine Learning Solution Lead at SingleStone. She is an accomplished data scientist who, in addition to leading data solutions at SingleStone, utilizes her expertise to drive social and economic change in communities.

How did you end up at SingleStone?

In 2018, I applied for the Partnership for the Future program because it offered students a chance to plan and gain skills for the future. I was paired with SingleStone based on my interest in technology. I am super excited to be a part of the team, learn about new technology and discover projects and topics that might influence what type of career I pursue.

So, are you doing actual work?

Yes! Over the past month, I’ve worked with several SingleStone teams. I’ve enjoyed learning about how teams work on projects, what they’re building, developing and working towards. It’s really interesting to witness how teams work together and how SingleStone, as a company, operates. Everyone has helped me feel included on projects and tasked me with actual work that is contributing to project goals. They are open to my ideas and have helped me gain a deeper understanding of what we’re trying to accomplish.

What’s your favorite thing about being here so far?

Working with people makes me really happy. And SingleStone has lots of people. The people here are kind, very smart and understanding. They are always open to helping me and answering any of my questions. Everyone is open-minded when it comes to new ideas and are always looking to introduce me to new things. They challenge themselves, and each other, to new possibilities. I constantly feel challenged to expand my horizons and think bigger. Every person I’ve met has told me they’re happy to have me here and I am so happy to be here!

What has being an intern taught you about yourself?

During my time here, each project and team has introduced me to new technologies and challenges. I always doubt myself, but they continuously tell me they believe in my abilities. Over time, they’ve given me more and more tasks that are out of my comfort zone because they know I can problem solve and figure things out. I have become more of a risk taker now and that has taught me a lot about myself and what I can accomplish.

What’s next?

Being at SingleStone has really made me think about my future. It has shown me what my career could turn out to be. Working with the different teams and people has brought out interests I didn’t know I had. It is definitely an eye-opening experience and one that has positively impacted my outlook on my abilities and future.

This experience is rare and makes me very thankful that I have such an amazing opportunity like this. I’m an intern at an awesome company that’s comprised of great people, who value me. I am always excited about the projects I get to work on and the people I get to work with. This experience is truly amazing and very beneficial for my future.

We’re incredibly fortunate to have Sarah on our team. Her presence challenges us to think about how we foster young talent and mentor each other. We’re looking forward to working with her for two more summers and watching her grow.

To learn more about Partnership For The Future and how you can help, visit their website.

We’re hiring. Visit our careers page to explore opportunities at SingleStone.

To learn more about the creation and importance of Cornhole for a Cause, we sat down with Tricia Rhodes, Employee Experience Lead.

How and why did Cornhole for a Cause begin?

Tricia: SingleStone is a people-first company; foundationally, we strive to create positive experiences in all of our interactions. We have and will always continue to put our clients and the people our solutions impact, at the forefront of our company objectives. A few years ago, we noticed a number of our employees were engaging in personal acts of charity or volunteer efforts. There was a desire to do something at the company level, where we could pool our resources for even greater community impact.

We brainstormed types of charity events that would fit our culture and resonate with our networks. We came up with hosting a cornhole tournament as it allowed us to invite our clients and friends to enjoy the outdoors, play a game, and support a great cause. We hosted our inaugural corn hole tournament in 2015, and haven’t stopped since.

How do you choose an organization to feature?

Our team members recommend organizations based on personal passions, and we all vote for our pick. The only requirements are that the organization is local and works with children in some way. Many of our employees have families and we have, as a company, often gravitated toward charitable organizations that support children and youth. This year, a team member suggested that we consider ASK, and after our entire team had a chance to hear about their work in supporting families that are impacted by childhood cancer, there was a unanimous interest in supporting their organization. In particular, we were blown away by ASK’s creative approach to supporting families. From funding camp tuition for children battling cancer, to getting patients involved with local sports and providing hospital patients with books and games to keep them entertained while they’re receiving treatment, ASK’s impact is diverse and powerful.

How do you measure success?

Success is comprised of the money raised, number attending and fun had by all. Our first year, we raised 6K+ for SCAN(greater RVA). The year after, we raised 9K+ for Sportable. And this year, we were excited to feature and support ASK. In addition to the growing monetary impact, we’ve seen engagement and excitement for the event increase year over year. Clients look forward to signing up for the tournament and have started including their family and friends.

Why is this an important event for SingleStone?

This event reminds us why we do what we do. We approach partners in the same way that we approach community — it’s about people and creating a positive impact, whether it’s a technology solution we’re delivering or a charity we’re supporting. This event is one of the many ways we can get together with our clients, friends, and coworkers to remind ourselves about the importance of giving back.

What’s next?

We’re already planning for next year, it’s going to be bigger and better with higher attendance and a larger monetary goal. There is always room to do and be more. Keep an eye out for upcoming events — come support your community, and meet some wonderful SingleStone employees!

At SingleStone , it’s not just about technology. It’s about creating technology that improves the lives of the people our solutions impact. Cornhole for a Cause is one of the many ways we can go beyond creating technology that provides a positive impact — we can make a direct and lasting impact on our community.

Tricia Rhodes is SingleStone’s Chief Employee Experience Officer and a leader in many of our philanthropic efforts. She is a champion of our corporate culture and has been key in creating a company where people are encouraged to contribute, rewarded for trying new things, and supported when taking risks.

How and why did you become interested in fintech?

Ben: Initially, I started looking at fintech because of my children. I have five kids and a few years ago I started to think about how I could foster financial literacy with each of them. As a technologist, myself, I thought there was likely a fintech tool out there that could help me. At the time, I was familiar with some of the popular banking apps and knew the industry was ripe for further innovation.

I more or less jumped right in…I decided to test robo-advising, which allows you to invest money and receive financial advice with little to no human interaction (hence “robo”). The idea of leveraging mathematical rules and algorithms for investments seemed almost safer than involving human decisions. I chose to try a portfolio with Betterment, and its been really interesting. I’m still learning how to properly utilize their product, but at this point, I’m pretty convinced this form of fintech could really alter the future of investing.

It sounds like fintech became a hobby-interest of yours?

Definitely, what started as a simple fact or app-finding journey to help my kids, quickly spiraled into a hobby. In recent years, I’ve immersed myself in fintech news and emerging innovations. In addition to consuming a lot of information and testing new products, I’ve also been sharing. Last month I contributed to the Fintech Daily podcast. And, I’m really excited about the episodes I will be hosting in the second half of 2019.

Every time I unearth a new tool or projected innovation, I’m struck by the ever-growing opportunity for tech to influence the financial world. The things I’m learning about today will be eclipsed in my children’s lifetime; they will be utilizing completely different tools and likely have a wildly different relationship with money. Take Facebook for example, they are really taking a leap with Libra. They could literally change the world if they are successful. Fintech is all about innovation, not iteration. The winners in that space won’t just be “moving the needle” but will impact generations. It’s really exciting.

Who is standing out?

There are a lot of altruistic ideas emerging today, like Aspiration. I’m curious to see how these ideas impact our way of viewing money and its impact on the world. Will tapping into someone’s desire to “do good” change where they invest? If so, what type of impact would that have on society? The possibilities are quite intriguing.

In regard to my kids, I’m thinking about enrolling my oldset son in a StockPile account. I’m curious to see his interest in the stock market and financial trends. He knows the big company names like Amazon and Google. Through StockPile, we can buy fractional shares if he doesn’t have the money to buy a full share (obviously, most of these stocks cost more than his monthly allowance). Instead of spending every dime he has on Fortnite, he can now see how money can actually grow (or shrink, if the market takes a downturn). I’m not sure I would have had an easy time creating excitement or interest in the stock market with my son, prior to applications like StockPile.

Thanks for the insights, Ben! Your excitement is contagious, and we’re excited to continue featuring your research and impact on the Fintech space.

Ben Nelson is a senior consultant at SingleStone, leading development teams that build custom applications. When he’s not advancing client solutions, he can be found spending time with his family or following his passion for new technology.

That makes us, as Data Scientists, veritable Air Benders. Our power is the ability to bend data to our will and purpose. Our challenge is to develop an almost spiritual adherence to neutrality in order to preserve the integrity of the data itself. We are the heroes to whom the citizenry has entrusted its thoughts, its money and even its caloric intake. And we are paid by corporations and other organizations to interpret the people via these data points. We teeter in a precarious position between these two groups: the citizenry that generates the data and the owners of the data infrastructure.

Human Management

I’ve been in the field since 1995. In the ensuing quarter of a century I’ve observedthat more often than not, data analysts and engineers are asked to manage humans. More specifically, we are asked to provide validation for actions, policies and procedures that will make the management of humans more efficient and profitable.

GPS

An example of human management from my own life is my reliance on my GPS application (whom I’ve affectionately named Evelyn) to chart my daily commuting pathways. Evelyn tells me when it’s time to leave for work every morning, adjusting for traffic to ensure my on-time arrival for the first meeting of the day. She charts alternate routes and will even find the closest gas station if I’m running low on fuel.

Most days, I blindly follow. Every now and then, though, I rebel. Why? Because I am a community girl. I love driving through my neighborhood. But even when it’s the fastest route, Evelyn routes me around my predominantly Black, lower income community instead of through it. Could it be, I wonder, that she’s using something besides traffic data to select my route?

Autocompletion

Another example of human management that I’ve recently experienced is Gmail’s autocompletion of my sentences. I didn’t think I had an intimate enough relationship with Google for us to finish one another’s sentences. And yet, Google wants to normalize my language to a 4thgrade version of vanilla flavored American English. My reaction is visceral: Just as my neighborhood shouldn’t be rendered invisible, my unique language and speech patterns shouldn’t be muted.

So then, what do we — the originators of such programs of management — do? Well, at the very least we start by asking “Why?” And, For What Outcome? And at the start of every single project, before we look at one element of data, we can do a simple cost benefit analysis of the potential use cases for the responses.

Creating an awareness for data bias is crucial to providing any form of statistical analysis. I provided just a couple of examples that you might encounter on a daily basis, but this is just the first step. In a followup to this post, I will dive deeper into what aspects of data can lead to a loss of neutrality and how to combat those instances.

Vida Williams is the Data & Machine Learning Solution Lead at SingleStone. She is an accomplished data scientist who, in addition to leading data solutions at SingleStone, utilizes her expertise to drive social and economic change in communities.

Data is Rarely Neutral: Bias in the Practice of Data was originally published in SingleStone on Medium, where people are continuing the conversation by highlighting and responding to this story.

Do you know how your organization’s teams are getting work done? Do you know how much value they’re producing each day? What about the factors that are driving their success — or failure?

If you can’t answer these questions, and provide numbers to backup your answers, chances are you’re leaving productivity and profit on the table every single day.In today’s rapidly evolving business environment, where teams are formed and reformed fluidly in order to get work done, most leaders have very little idea of what their company’s team structure looks like, how work is getting done, or how value is being generated (or depleted) — even as they remain responsible for creating that value.If that’s you, you’re not alone. And it’s not your fault.

When researching what makes for team success, hard numbers and concrete advice are hard to find. This topic has been well researched, but the results are conceptual and difficult to implement, requiring significant cultural change without evidence of results. Most discouragingly, though research agrees on some common qualities of successful teams, what actually makes for consistent team success and failure is different for each company, and changes based on factors like industry, product and business model.

The damaging result of this is that when you want to know what makes the difference between your teams succeeding or failing (creating or draining value), you only have a few options: guess (really), rely on tribal knowledge, or slog through historical data to come up with stale numbers. This leads to decision lag and an inefficient and costly trial-and-error approach, leaving leaders vulnerable and resulting, ultimately, in missed value.

If this is sounding all too familiar, don’t panic. You can get a true grip on what’s happening in your teams, and where you can drive productivity and value across your organization.

“One of the hardest things for technology leaders and managers today, is that there’s plenty of data out there, but it’s hard to get a clear idea of what’s driving team success,” explains Kevin Tuskey, SingleStone’s Director of Design. “Even if you’re not starting from scratch, the products and tools available just aren’t designed to show you what’s important.”

Suzanne Hawthorne, Account Director and Client Advisor at SingleStone agrees: “Even our most technologically advanced clients are struggling in this space. Measuring team success across an entire enterprise is tough, but it’s crucial to today’s leadership if they want to make the quick, data-driven decisions that will help their company get ahead. It’s the void in this critical area that drove us to create Team Insights.”

Team Insights, SingleStone’s team intelligence product, takes the guesswork out of what makes a company’s Agile teams successful. Instead of merely showing the HR structure of an organization, it shows how a company’s cross-functional teams are organized in a simple, easy-to-use system. Team Insights provides data-based decisions to drive how to get work done and create value. In short, it’s built to capture the data that helps a company and its leadership pinpoint, measure and unleash untapped productivity in their Agile organization.

“Before Team Insights, there was nothing like this available in the marketplace for our clients, and it was a huge gap,” says Hawthorne. “This product is designed to help our clients do three things: Gain a clear line of sight to the way work gets done in their company, capture straightforward data on the factors that contribute to team success, and uncover trends and insights that drive confident action and proactive decision-making.”

If you are struggling to gain line of sight to your Agile organization, or to make the data-driven decisions that will take your company to the next level, SingleStone can help. Our consultants and technology teams bring more than 20 years of experience in the areas of data, software, team success and Agile transformation. Our custom dashboards power decision making at many Fortune 500 companies in financial services and other industries. Team Insights is a direct result of our collective experience and track record.

Make 2019 your year to get on track and unleash the hidden potential of your teams with Team Insights. Reach out to learn more about our team intelligence product and schedule a free demo.

Unleash the Power of Your Teams was originally published in SingleStone on Medium, where people are continuing the conversation by highlighting and responding to this story.

by John Chapin

1. Serverless doesn’t require management of Server hosts or Server processes.

Where Serverless goes beyond a traditional Platform as a Service (PaaS) — and most usages of containers — is the removal of the concept of a long-lived ‘server’ component that we define. Instead, we configure capacity — in the case of Backend as a Service (BaaS) — and we define event-driven functions — in the case of Functions as a Service (FaaS) — but there is no long-lived process to manage ourselves in the Serverless model. We no longer have to think about the number of server instances we need for a given component and we can skip defining the type or instance size our application requires to run.

This is why we call it Serverless: We are building server-side software solutions, but the servers themselves are all abstracted. They still exist somewhere behind the scenes, but we don’t have to think about them.

2. Serverless architectures auto-scale and auto-provision based on load.

With traditional server-side architectures we are responsible for provisioning and scaling the resource usage of our applications. But Serverless changes all this — they auto-provision the required resources. In other words, as soon as you start using the service, it figures which resources you require and how much of each resource you need and automatically provisions them for you.

Self auto-scaling and auto-provisioning save time and effort. This is especially helpful when you’re developing a new product or service and need fast time to market. Serverless architectures also often reduce infrastructure costs because this automatic scaling is right sized.

3. The metered cost of Serverless architectures is very precise.

Serverless services are billed on very precise usage metrics. Consider Elastic Compute Cloud (EC2) from Amazon Web Service (AWS) as a comparison. EC2 provides scalable computing capacity and is billed on a per-second basis (whether the EC2 instance is performing any useful work or not). In contrast, Lambda — AWS’s event-driven Serverless computing platform — is priced based on the number of requests you receive per month, the duration of those requests, and the amount of memory the functions are configured for. Requests are billed at $.20 per 1 million requests. Duration is billed per 100 millisecond and is calculated from the time your code begins executing until it returns or otherwise terminates.

Thanks to these precise usage costs and the self auto-provisioning and auto-scaling features mentioned above, Serverless systems are charged more efficiently and reactively than other deployment architectures.

4. You can’t define Services performance capabilities in terms of host size/count.

Since we’re not choosing the number, type or size of servers in our Serverless architecture, how do we define the performance requirements that we need in order to run our Serverless applications?

With AWS Lambda we have one performance dial: RAM. You can’t say what overall speed CPU you want or that you want more cores, or that you want a different amount of local disk storage.

With Azure App Service Plans, there are a few more dials to turn, but that quickly begins to feel like you’re managing servers again. In some ways this is not a new problem (think virtualization and containerization), but it does point to the immaturity of Serverless architectures and we expect configuration controls to expand in the future.

5. Serverless offers implicit high availability.

The term high availability (HA) refers to software’s ability to continue to operate even when one instance of a component fails. We often implement HA using some kind of redundancy. With Serverless, this redundancy is implicit, but it does have its limits. On AWS, a Serverless application based on Lambda has the ability to handle the failure of an entire availability zone, which roughly maps to a datacenter. However, Serverless does not offer implicit Disaster Recovery. We still need to consider disaster scenarios in our architectural planning.

John Chapin is a co-founder of Symphonia, and will be leading a one-day Serverless workshop in Richmond, VA, on November 20th. For more information or to buy tickets, visit Reverb.

This piece was inspired by a five-part blog called Defining Serverless, written by Chapin’s co-founder Mike Roberts.

Their highly regarded talks and workshops are regularly featured at conferences, including the Software Architecture Conference, Velocity, OSCON, QCon, ServerlessConf and AWS re:Invent.

Serverless was originally published in SingleStone on Medium, where people are continuing the conversation by highlighting and responding to this story.