Photo by Nick Fewings on Unsplash

I’ve been in the tech community for over 20 years as a degrees-holding computer scientist. I’ve taught, advised and mentored those aspiring to those seasoned in computing concepts for nearly 15 years. The learning and development sessions, lectures and courses harped on algorithmic design, computational thinking, computer programming language similarities and differences, effective coding practices and knowing fundamental data structures and algorithms. And because I am a data nerd, many of my lessons would cover data modeling, entity-relationship diagramming, SQL, data stewardship and data integrity concepts. Students and mentees asked plenty of excellent questions about the mechanics of coding and database management, which were rather straightforward to answer. There are various textbooks, static websites, online tutorials and YouTube videos to help demonstrate why my response was my response.

But, in time as my students and mentees became more comfortable with the coding and data management mechanics, a new band of questions came that wasn’t readily available on the interwebs — how does the nature of their coding and data management efforts impact society. They were no longer pushing out lines of code and data models and happy about it. They became acutely aware that their work was being scaled and affected thousands to millions of people. Their analytical thinking deductions collided with their creative thinking intuitions. If you’re in the tech community long enough, you reach this junction point. You feel like you have to decide either stay the left-brain path of analytical thinking or switch over to the right-brain path of creative thinking.

Nope, there’s another option to stay at the intersection.

I am an analytical thinker with creative tendencies. As a computer science data nerd, I am regularly trying to reconcile the structure imposed by computing with the constant changing status of the data industry. This intersection of analytical and creative thinking is questioning the practicality of computing and data management theories and critiquing the deployed risk mitigation strategies in handing an organization’s data operations. The goal is to learn to effectively and constantly adjust our perspectives on algorithmic-based outputs and how data operations are handled given the current context. This means that sometimes the output based on coded algorithms becomes unusable as the context shifts since the code was developed. Or that a data operation, like data collection, which you thought was ethical, now no longer mets your organization’s responsible data practice criteria. You learned that the data collection practice compounded harms to a certain community.

This intersection of analytical and creative thinking isn’t easy.

If you work long enough with lines of code or engage often enough with data management, you too will discover tech-based harms due to scaling of algorithms and mishandling of data to communities who has suffered for generations. You feel like there’s a moving target. And frankly there is a moving target as data is always in a state of flux and your understanding of context evolves as you expand what you know.

It’s a scary realization, especially when you don’t recognize the parallels with how we currently engage with our tech tools. You download and install the tech tool — let’s say it’s an online banking app to make this example more concrete. As you use the online banking app, the company who built the app receives data and information on how you and others use their app. They learn from these data insights and make modifications in order to make the experience better for their customers. These modifications, which we commonly call ‘software updates’, are shared with us. And the download-use-modify cycle repeats…indefinitely. So see, we’re already integral to the moving target movement. Essentially, tech’s revision processes are a blueprint for including ethical and responsible practices in data operations.

The challenge for the tech community is in how ethical and responsible practices in data operations are measured. By designing and deploying a tech tool, the advantages are evaluated based on usage patterns and frequency of usage. The tech community has been a bit spoiled since they can use quantitative metrics to be a proxy for impact. Responsible data operations and mitigating algorithmic-based harms requires a reliance on qualitative metrics. The quality of their impact needs to be formed by a consensus across different communities. If a community does experience harms, then the community’s response is to not engage with that tech and the tech community has little to no (quantitative or qualitative) data to recognize their absence — unless they’ve established intentional and authentic relationships over years, not 1–2 Zoom calls, in order to design effective harm reduction strategies.

I was lonely for years being at this intersection of analytical and creative thinking. But now, there are more and more tech people joining to combat algorithmic-based harms and infuse more equity in to all data operations. If we could only receive the fiscal support to match this momentum, we’d experience some sustainable breakthroughs.