The paper discusses extending the Predict-Then-Optimize (PtO) methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. OWA objectives ensure fairness and robustness in decision models. The authors propose…
Continue readingThis academic paper by Jazmia Henry from the University of Oxford investigates the concept of fairness in Artificial Intelligence (AI) through the lens of dictator games, a type of utilitarian…
Continue readingThe article presents F AIRDEBUGGER, a system designed to identify and explain instances of fairness violations in the outcomes of random forest classifiers. It uses machine unlearning to estimate changes…
Continue readingThe research presents CP-FairRank, a re-ranking algorithm that integrates fairness constraints from both consumer and producer perspectives. It aims to improve fairness in recommender systems, which are widely used in…
Continue readingThis academic article presents a new approach to counterfactual reasoning in fairness and recourse within artificial intelligence (AI) systems. Traditionally, counterfactual reasoning involves imagining hypothetical interventions on legally-protected characteristics such…
Continue readingFalcon is a novel framework that aims to address the issue of bias in machine learning models. The framework employs a data-centric approach, focusing on the curation and labeling of…
Continue readingThis paper presents MAPPING, a model-agnostic framework for debiasing Graph Neural Networks (GNNs) to ensure fair node classification and reduce leakage of sensitive information. Despite the success of GNNs in…
Continue readingThis article presents a study examining the fairness of self-supervised learning (SSL) models compared to their supervised counterparts. The researchers hypothesized that SSL models would be less biased, and their…
Continue readingThe paper introduces a new benchmark, FFT, for assessing the harmlessness of Large Language Models (LLMs). It addresses concerns about the potential harm caused by AI-generated texts, which can stem…
Continue readingThe study discusses the potential harms posed by AI-generated texts, especially from factoid, unfair, and toxic content. The authors propose FFT, a new benchmark with 2116 instances, for evaluating the…
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