arXiv:2607. 26908v1 Announce Type: new Abstract: In many domains such as Palliative Care, Credit Assignment and Recommender Systems, predictions may causally influence the outcomes they predict.
By Brandon Gower-Winter, Georg Krempl
arXiv:2505. 08908v3 Announce Type: replace-cross Abstract: Many researchers apply classical statistical decision theory to evaluate treatment choices and learn optimal policies.
By Benedikt Koch, Kosuke Imai
arXiv:2601. 17146v2 Announce Type: replace-cross Abstract: Empirical investigations into unintended model behavior often show that the algorithm is predicting another outcome than what was intended.
By Amanda Coston
arXiv:2607. 21806v1 Announce Type: new Abstract: Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and criminal justice.
By Jonathan Zhang, Erik Skalnes, Jacob Chen, Michael Oberst
The paper evaluates machine learning models that predict retention and premature discontinuation in medication for opioid use disorder (MOUD). Using the Treatment Episode Data Set-Discharges (TEDS‑D) from 2015‑2019, the authors trained four models and examined overall performance as well as subgroup error rates by race, ethnicity, age, and sex. They also tested bias‑mitigation techniques, finding that these can reduce but not eliminate performance gaps without harming predictive accuracy.
By Tongnian Wang, Carolina Vivas-Valencia, Cici Bauer, Yanmin Gong, Kim-Kwang Raymond Choo, Yuanxiong Guo
The paper argues that traditional probabilistic fairness metrics can miss significant disparities in the actual consequences of decisions. By introducing a utility-based framework, the authors show that a process can satisfy ε-fairness yet still be maximally unfair when utilities are considered. They apply this framework to college admissions and credit‑risk assessment, demonstrating that equalizing probabilities alone may mask unequal utility outcomes across groups.
By Tolulope Fadina, Thorsten Schmidt