The Importance of Algorithmic Fairness - IT Peer Network

Algorithmic fairness is a motif that plays throughout our podcast series: as we look to AI to help us make consequential decisions involving people, guests have stressed the risks that the automated systems that we build will encode past injustices and that these decisions may be too opaque. In episode twelve of the Intel on AI podcast, Intel AI Tech Evangelist and host Abigail Hing Wen talks with Alice Xiang, then Head of Fairness, Transparency, and Accountability Research at the Partnership on AI--a nonprofit in Silicon Valley founded by Amazon, Apple, Facebook, Google, IBM, Intel and other partners. With a background that includes both law and statistics, Alice's research has focused on the intersection of AI and the law. "A lot of the benefit of algorithmic systems, if used well, would be to help us detect problems rather than to help us automate decisions." Algorithmic fairness is the study of how algorithms might systemically perform better or worse for certain groups of people and the ways in which historical biases or other systemic inequities might be perpetuated by AI.