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 Tẹjúmádé Àfọ̀njá unpacks why more accurate predictions in a model 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.

Read Tẹjúmádé's full papers here:

Paper on loan approvals: https://aclanthology.org/anthology-files/pdf/findings/2025.findings-emnlp.947.pdf Paper on World Wide Dishes: https://dl.acm.org/doi/full/10.1145/3715275.3732019

More about her and her research: https://tejuafonja.com

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