Fairness in NLP¶
Fairness in NLP addresses the challenge of ensuring that natural language processing systems do not discriminate against or treat unfairly individuals or groups based on protected attributes (e.g., race, gender, age, disability status). This is both a technical research area and a critical requirement for responsible AI deployment.
Research in fairness spans several dimensions: (1) defining fairness (which fairness criterion is appropriate for a given context?), (2) measuring fairness (how do we quantify unfairness or bias?), (3) understanding mechanisms (why do models exhibit bias?), and (4) mitigation (how can we reduce bias while maintaining utility?).
In the context of language models specifically, fairness concerns arise because these models are increasingly used in high-stakes applications—resume screening, loan decisions, content moderation, hiring—where discriminatory outputs can have serious real-world harms. Moreover, language models' vast capacity to encode social information makes them particularly prone to learning and amplifying societal biases.
Key papers¶
- A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability — Comprehensive survey of fairness in graph neural networks, covering fairness approaches spanning pre-processing (attribute modification), in-processing (representation learning constraints), and post-processing (prediction calibration) to ensure non-discriminatory predictions across protected attributes.
Related topics¶
- Bias in Language Models (specific focus on stereotyping and discrimination)
- Model Alignment (alignment with ethical values including fairness)
- AI Safety (fairness as a component of safe AI)