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Found 10 papers, 2 papers with code
Contextual bandits with concave rewards, and an application to fair ranking
We consider Contextual Bandits with Concave Rewards (CBCR), a multi-objective bandit problem where the desired trade-off between the rewards is defined by a known concave objective function, and the reward vector depends on an observed stochastic context.
Fast online ranking with fairness of exposure
In this paper, we propose the first efficient online algorithm to optimize concave objective functions in the space of rankings which applies to every concave and smooth objective function, such as the ones found for fairness of exposure.
Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and law
The contextualization will focus on the legal systems of the United States on the one hand and Europe on the other.
Optimizing generalized Gini indices for fairness in rankings
Depending on these weights, GGFs minimize the Gini index of item exposure to promote equality between items, or focus on the performance on specific quantiles of least satisfied users.
Two-sided fairness in rankings via Lorenz dominance
Our experiments also show that it increases the utility of the worse-off at lower costs in terms of overall utility.
Online Selection of Diverse Committees
Citizens' assemblies need to represent subpopulations according to their proportions in the general population.
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
e-ViL is a benchmark for explainable vision-language tasks that establishes a unified evaluation framework and provides the first comprehensive comparison of existing approaches that generate NLEs for VL tasks.
Online certification of preference-based fairness for personalized recommender systems
We propose to audit for envy-freeness, a more granular criterion aligned with individual preferences: every user should prefer their recommendations to those of other users.
e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language Explanations
The recently proposed SNLI-VE corpus for recognising visual-textual entailment is a large, real-world dataset for fine-grained multimodal reasoning.
