Episode 46 of CISPA TL;DR: Fairness and Representation in AI with Tejúmádé Àfònjá

From job applications to loan approvals, AI systems are increasingly being explored and deployed in decisions that shape people’s lives. But what happens when these systems learn from biased data? Can they ever be truly fair?
In this episode, CISPA researcher Tejúmádé Àfònjá explains why more accurate predictions don’t automatically mean fairer outcomes, why representation in AI and machine learning matters, and why it’s not only important how AI systems are built – but also by whom. 
 
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