Neural Information Processing Systems http://nips.cc/

Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy.

Neural Information Processing Systems http://nips.cc/

In order to build more useful AI systems, a natural inclination is to try to make them more agentic . But while agents built from language models are touted as the next big advance [Wang et al., 2024],

Whereas Perdomo et al. care only about predictive accuracy, we care also about the quality of the actual outcomes10 associated with decisions, and study the tradeoff between decision improvement and predictive accuracy. Consider,forexample, mortgage13 buyers, ICU patients, orfirst-time medical consultation (e.g., oncology,cardiology,psychiatry,screening tests). Thisisacausalproblem,and18 as such, requires assumptions that ensure causal validity (in our work this comes in through our use of propensity19 scoresforreweightingevidence). Undesired outcomes, as in the policing example, are the result of30 sample bias indata (missing observations) andoftheinappropriate useofpredictivetools fordecision making.