Operationalizing AI Ethics, No Longer An Option But An Imperative

As I've written in my "On AI Ethics," series, machine learning models that aim to mirror and predict real-life as closely as possible are not without their challenges. Household name brands like Amazon, Apple, Facebook, Google have been accused of algorithmic bias that have negatively affected society at large. While some organizations are investing in teams to ensure algorithmic accountability and ethics, Reid Blackman, CEO of Virtue and former professor of philosophy at Colgate University and the University of North Carolina, Chapel Hill, says most are still falling short in ensuring their products perform ethically in the real world. "Despite reputational, regulatory, and legal risks, it's surprising how many companies that rely on AI/ML still lack the ability to identify, evaluate, and mitigate the associated ethical risks," says Blackman. "Teams end up either overlooking risks, scrambling to solve issues as they come up, or crossing their fingers in the hope that the problem will resolve itself."