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

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 🧵👇

🚨 Excited to announce a new paper accepted at #ICLR2025 in Singapore! “Learning Under Temporal Label Noise” We tackle a new challenge in time series ML: label noise that changes over time 🧵👇 arxiv.org/abs/2402.04398