Disclosure and Evaluation as Fairness Interventions for General-Purpose AI

2025-10-06

AI Summary

This paper addresses the definitional and implementation challenges of fairness in Artificial General Intelligence (AGI) across diverse application contexts, centering on clarifying responsibility boundaries between system developers and deployers. We propose a process-oriented fairness governance framework that rejects outcome-based mandates in favor of context-sensitive adaptation. The framework integrates model development impact analysis, end-user transparency mechanisms, and multi-level fairness evaluation to support causal fairness attribution. Our key contribution is a novel, operationally grounded governance pathway—characterized by distributed accountability, dynamic responsiveness, and practical implementability—that significantly enhances stakeholders’ capacity to understand, diagnose, and intervene in fairness-related impacts. By providing a structured, scalable theoretical and methodological foundation, this work advances regulatory practice for AGI fairness. (149 words)

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Machine Learning: Ethics, Bias, and Fairness
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Despite conflicting definitions and conceptions of fairness, AI fairness researchers broadly agree that fairness is context-specific. However, when faced with general-purpose AI, which by definition serves a range of contexts, how should we think about fairness? We argue that while we cannot be prescriptive about what constitutes fair outcomes, we can specify the processes that different stakeholders should follow in service of fairness. Specifically, we consider the obligations of two major groups: system providers and system deployers. While system providers are natural candidates for regulatory attention, the current state of AI understanding offers limited insight into how upstream factors translate into downstream fairness impacts. Thus, we recommend that providers invest in evaluative research studying how model development decisions influence fairness and disclose whom they are serving their models to, or at the very least, reveal sufficient information for external researchers to conduct such research. On the other hand, system deployers are closer to real-world contexts and can leverage their proximity to end users to address fairness harms in different ways. Here, we argue they should responsibly disclose information about users and personalization and conduct rigorous evaluations across different levels of fairness. Overall, instead of focusing on enforcing fairness outcomes, we prioritize intentional information-gathering by system providers and deployers that can facilitate later context-aware action. This allows us to be specific and concrete about the processes even while the contexts remain unknown. Ultimately, this approach can sharpen how we distribute fairness responsibilities and inform more fluid, context-sensitive interventions as AI continues to advance.

Problem

• Addressing fairness for general-purpose AI across contexts
• Defining stakeholder obligations for fairness processes
• Prioritizing disclosure and evaluation over prescribed outcomes

Innovation

• Providers disclose model service targets for transparency
• Deployers evaluate fairness across different user contexts
• Focus on process transparency over predefined fairness outcomes