Prediction without Preclusion – Berk Ustun (UC San Diego)

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 DiegoAvni Kothari – University of California, San DiegoBogdan Kulynych – Lausanne University Hospital Tsui-Wei Weng – Halıcıoğlu Data Science InstitutePrediction without Preclusion: Recourse Verification with Reachable Setshttps://arxiv.org/abs/2308.12820Audio Credits for Trailer:AllttA by AllttA https://youtu.be/ZawLOcbQZ2w