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Found 7 papers, 2 papers with code
Does AI help humans make better decisions? A methodological framework for experimental evaluation
We consider a single-blinded experimental design, in which the provision of AI-generated recommendations is randomized across cases with a human making final decisions.
Policy Learning with Asymmetric Counterfactual Utilities
We consider optimal policy learning with asymmetric counterfactual utility functions of this form that consider the joint set of potential outcomes.
Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment
We extend this approach to common and important settings where humans make decisions with the aid of algorithmic recommendations.
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied.
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
While several cross domain sequential recommendation models have been proposed to leverage information from a source domain to improve CTR predictions in a target domain, they did not take into account bidirectional latent relations of user preferences across source-target domain pairs.
Principal Fairness for Human and Algorithmic Decision-Making
Using the concept of principal stratification from the causal inference literature, we introduce a new notion of fairness, called principal fairness, for human and algorithmic decision-making.
