Can AI Really Be Fair?

My Thoughts on Designing Equal Opportunities

After listening to the podcast “AI Fairness: Designing Equal Opportunity Algorithms” with Professor Derek Leben and Dr. Waseem Akhtar on Bridging the Gaps, my takeaway is that AI fairness is not a technical problem but a design and human problem. Humans build AI systems/agents, train them on human-created data, and use them to make decisions that affect humans. Thus, fairness should be a concern from the outset of the design process and not something that can be corrected after the system is built. We should design AI systems/agents that create equal opportunities.

As a UX designer, I found this interesting because we have the responsibility to think about who might be affected by the product we are working on. Just because a system has a clean interface and functions perfectly from a technical perspective does not mean that it is necessarily inclusive or fair. “I think designers and developers should consider different users, challenge the data they use, test systems with different groups, and understand the possible consequences of automated decisions.” So, fairness needs to be implemented at the starting point (Research & Design) phase, rather than after the system is built.

And I think the most interesting thing is that there may not be a single simple definition of fairness. “So, one metric might say an AI system is treating two groups the same, but it might still have an unfair outcome in some other way. There’s no single rule to make AI fair for everybody. That means AI fairness requires ongoing debate, testing, and human judgment, not just following a single rule.

Thanks for Reading

I hope you’ve learned something.😊

We will meet in the next article, and please appreciate this with your precious hand claps. Till then, have some fun.🤞

Let’s get connected on LinkedIn.

Can AI Really Be Fair? was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.