Prediction without Preclusion – Berk Ustun (UC San Diego)
Автор: Center for Law & Economics ETH Zurich
Загружено: 2026-02-17
Просмотров: 17
Описание:
In this episode of the CLE Vlog & Podcast Series, Prof. Berk Ustun (University of California San Diego) discusses his paper "Prediction without Preclusion: Recourse Verification with Reachable Sets" with Benjamin Kohler (ETH Zurich).
In their work, Berk Ustun and his co-authors investigate how machine learning models in high-stakes settings, such as lending and hiring, assign "fixed" predictions that individuals cannot change regardless of their actions. They introduce a formal procedure called "recourse verification" to certify whether a model allows for responsiveness or precludes access. In addition, they develop an auditing tool for practitioners to flag models that effectively block access to certain outcomes before they are deployed – a crucial step to promote fairness and transparency in machine-led decision making.
Paper Reference:
Berk Ustun – University of California, San Diego
Avni Kothari – University of California, San Diego
Bogdan Kulynych – Lausanne University Hospital
Tsui-Wei Weng – Halıcıoğlu Data Science Institute
Prediction without Preclusion: Recourse Verification with Reachable Sets
https://arxiv.org/abs/2308.12820
Audio Credits for Trailer:
AllttA by AllttA
• AllttA (@20syl & @mrjmedeiros ) - AllttA (...
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