In a new paper, I try to resolve the counterintuitive evidence of Meehl’s “clinical vs statistical prediction” problems: Statistics only wins because the game is rigged.

When RAG systems hallucinate, is the LLM misusing available information or is the retrieved context insufficient? In our #ICLR2025 paper, we introduce "sufficient context" to disentangle these failure modes. Work w Jianyi Zhang, Chun-Sung Ferng, Da-Cheng Juan, Ankur Taly, @cyroid.bsky.social

Hey AI folks - stop using SHAP! It won't help you debug [1], won't catch discrimination [2], and makes no sense for feature importance [3]. Plus - as we show - it also won't give recourse. In a paper at #ICLR we introduce feature responsiveness scores... 1/ arxiv.org/pdf/2410.22598

Many ML models predict labels that don’t reflect what we care about, e.g.: – Diagnoses from unreliable tests – Outcomes from noisy electronic health records In a new paper w/@berkustun, we study how this subjects individuals to a lottery of mistakes. Paper: bit.ly/3Y673uZ 🧵👇

Denied a loan, an interview, or an insurance claim by machine learning models? You may be entitled to a list of reasons. In our latest w @anniewernerfelt.bsky.social @berkustun.bsky.social @friedler.net, we show how existing explanation frameworks fail and present an alternative for recourse