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#46 Fairness and Representation in AI with Tẹjúmádé Àfọ̀njá
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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By Annabelle Theobald und Tobias Ebelshäuser im Gespräch mit CISPA-ForschendenFrom 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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