Navigating The Challenges Of AI Fairness, Bias And Robustness

In the past several years, artificial intelligence ("AI") has exploded into the public consciousness and emerged as a driving economic force that underpins some of the world's largest companies and most exciting new start-ups. The emergence of AI in widespread commercial applications quickly led AI researchers within academia and industry to realize the potential risks of deploying these algorithms in real-world settings. Alongside the stunning successes of modern AI, concerns around bias, discrimination, and robustness of AI algorithms quickly proliferated.

Machine learning algorithms which performed reliably in testing, sometimes proved to be brittle and lack the level of robustness required when faced with the complexity of deployment in real-world environments. Similarly, as machine learning was increasingly incorporated into systems that made predictions about people, it became clear that AI models had a tendency to replicate, and sometimes amplify, biases