More AI Developers Focused on Engineering the Bias Out of AI - AI Trends

With AI systems today determining whether someone can get a job or a loan, it's in the interest of the company running the AI system to make sure the underlying dataset is not so biased that it leads to errors in its conclusions. Cases of biased data leading to biased results have been documented, such as in the research of Joy Buolamwini and Timnit Gebru, authors of a 2018 study that showed facial-recognition algorithms were very good at identifying white males, but recognized Black females only two thirds of the time. If law enforcement is using such a system to identify suspects, that can lead to some serious problems. The stage is set for serious effort to go into reducing biased datasets on which AI systems rely. "It's an opportunity," stated Alexandra Ebert, chief trust officer at Mostly AI, a startup focused on synthetic data based in Vienna, quoted in a recent account in IEEE Spectrum.