The Impossibility of Fair LLMs

2024-05-28

arXiv.org

AI Summary

Traditional machine learning fairness frameworks—such as group fairness and fair representation—are theoretically and practically inadequate for governing fairness in large language models (LLMs), due to their generative nature, context dependence, and open-ended output space. Method: We first establish the theoretical infeasibility of LLM fairness under rigorous formal definitions; then propose a use-case-centered approach grounded in contextual sensitivity, developer accountability, and iterative stakeholder co-design. Drawing on fairness theory critique, interdisciplinary socio-technical analysis, and AI alignment principles, we identify structural limitations inherent to LLMs. Contribution/Results: We develop an application-oriented fairness practice guide and introduce a novel paradigm leveraging LLMs’ own capabilities for fairness monitoring and governance—thereby shifting from abstract fairness metrics to context-aware, participatory, and self-augmented fairness stewardship.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

The need for fair AI is increasingly clear in the era of general-purpose systems such as ChatGPT, Gemini, and other large language models (LLMs). However, the increasing complexity of human-AI interaction and its social impacts have raised questions of how fairness standards could be applied. Here, we review the technical frameworks that machine learning researchers have used to evaluate fairness, such as group fairness and fair representations, and find that their application to LLMs faces inherent limitations. We show that each framework either does not logically extend to LLMs or presents a notion of fairness that is intractable for LLMs, primarily due to the multitudes of populations affected, sensitive attributes, and use cases. To address these challenges, we develop guidelines for the more realistic goal of achieving fairness in particular use cases: the criticality of context, the responsibility of LLM developers, and the need for stakeholder participation in an iterative process of design and evaluation. Moreover, it may eventually be possible and even necessary to use the general-purpose capabilities of AI systems to address fairness challenges as a form of scalable AI-assisted alignment.

Problem

• Evaluating fairness in LLMs with rigorous definitions
• Challenges in applying fairness frameworks to general-purpose AI
• Developing scalable fairness methods for diverse human-AI interactions

Innovation

• Analyzing various technical fairness frameworks
• Identifying inherent challenges in fairness frameworks
• Proposing iterative participatory AI-assisted evaluation methods