fairness

This paper introduces Counterfactual Explanation Consistency (CEC), a framework to detect and mitigate hidden procedural bias in outcome-fair models by aligning feature attributions between individuals and their counterfactual counterparts, with experiments on credit and income datasets.

This paper presents a systematic evaluation of how differential privacy impacts social bias in large language models, finding that while it reduces bias in sentence scoring, the effect does not generalize across all tasks.

FairHealth is an open-source Python library designed for trustworthy healthcare AI in low-resource settings, offering modules for fairness auditing, privacy-preserving federated learning, and explainability.

This study reveals a 'Smart Pruning Paradox' where activation-aware pruning methods like Wanda preserve perplexity but significantly amplify bias in Large Language Models deployed on edge devices.

This paper introduces Pareto UCB1 Gossip and Simulated NSW UCB Gossip for multi-objective multi-agent multi-armed bandits, addressing both learning efficiency and fairness in stochastic environments.

This paper critiques the use of single-reference ground truth in ASR evaluation, arguing it causes epistemic injustice for speakers with aphasia. It proposes a new metric, Epistemic Injustice Distance, and advocates for WER-Range to account for diverse transcription conventions.

MIT researchers release the first multilingual negation benchmark covering seven languages and show VLMs like CLIP struggle with non-Latin scripts, while MultiCLIP and SpaceVLM offer uneven improvements across languages.

DART (Distill-Audit-Repair Training) is a new training framework that addresses 'harm drift' in safety-aligned LLMs, where fine-tuning for demographic difference-awareness causes harmful content to appear in model explanations. On eight benchmarks, DART improves Llama-3-8B-Instruct accuracy from 39.0% to 68.8% while reducing harm drift cases by 72.6%.

Mediator.ai is a tool that applies Nash bargaining game theory and LLMs to facilitate fair cooperative negotiation, generating and scoring candidate agreements against both parties' stated needs until an optimal solution is found.

MIT researchers introduce SEED-SET, a framework using LLMs to proactively evaluate the ethical alignment of autonomous systems in high-stakes scenarios like power distribution, addressing gaps in static testing methods.