AI does many things far better than we do, but ensuring fairness involves decisions on our values, some of which are unaligned and nearly impossible to harmonize: can AI reduce us, our humanity, and everything that makes us tick, into a collection of data points? Or will there always be an elusive “ghost in the machine” that AI will never quite capture?

According to AI ethicist Dr. Ravit Dotan, Founder and CEO of TechBetter, there’s more to redressing ingrained biases than just enriching the data sets. To summarize her response: To quantify fairness, you must define what is fair, which is complicated. Companies already have certain values regarding equity and fairness, but they haven't figured out a way to measure them. The question then becomes: What outcomes are you measuring? What are the complicating factors, and what are the unintended consequences? In the study on mortgage loan fairness, they found that improving loan approval among blacks had a negative impact on the size of the loan. Ultimately, fairness reflects how we think about ethics, inclusion, and other values that define us. Believing that bias is simply a data problem to be solved with better numbers, misses the bigger picture. For more, check out the AIX Files newsletter: https://aixchronicles.substack.com/p/thinking-ai-a-formula-for-learning