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Fairness

3337 directly classified papers

Papers per year

Papers

Practising responsibility: Ethics in NLP as a hands-on course EACL 2026
ProbLog4Fairness: A Neurosymbolic Approach to Modeling and Mitigating Bias AAAI 2026
The Pluralistic Moral Gap: Understanding Moral Judgment and Value Differences between Humans and Large Language Models EACL 2026
Minimizing Inequity in Facility Location Games AAAI 2026
Fair Societies: Algorithms for House Allocations AAAI 2026
Online Fair Allocations with Binary Valuations and Beyond AAAI 2026
How Important is ‘Perfect’ English for Machine Translation Prompts? EACL 2026
SoS: Analysis of Surface over Semantics in Multilingual Text-To-Image Generation EACL 2026
Operationalizing Pluralistic Values in Large Language Model Alignment Reveals Trade-offs in Safety, Inclusivity, and Model Behavior AAAI 2026
A Tale of Two Identities: An Ethical Audit of AI-Crafted Synthetic Personas AAAI 2026
Capacity Constraints Make Admissions Processes Less Predictable AAAI 2026
CCD-Bench: Probing Cultural Conflict in Large Language Model Decision-Making AAAI 2026
The Illusion of Fairness: Auditing Fairness Interventions in Algorithmic Hiring with Audit Studies AAAI 2026
Achieving Fairness Without Harm via Selective Demographic Experts AAAI 2026
Fair Graph Learning with Limited Sensitive Attribute Information AAAI 2026
Voting in Divisible Settings: A Survey AAAI 2026
The Cost and Complexity of Minimizing Envy in House Allocations (Abstract Reprint) AAAI 2026
Conceptual Cultural Index: A Metric for Cultural Specificity via Relative Generality EACL 2026
Do Large Language Models Adapt to Language Variation across Socioeconomic Status? EACL 2026
SAFARI: A Community-Engaged Approach and Dataset of Stereotype Resources in the Sub-Saharan African Context EACL 2026
Beyond Bias Scores: Unmasking Vacuous Neutrality in Small Language Models EACL 2026
Colorism in Multimodal AI: An Empirical Exploration of Socioeconomic Linguistic Bias in Text-to-Image Generation EACL 2026
Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs EACL 2026
Don’t Judge Code by Its Cover: Exploring Biases in LLM Judges for Code Evaluation EACL 2026
Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations EACL 2026