Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge

2025-02-01

AI Summary

Evaluating generative AI (GenAI) systems faces core challenges: difficulty in validating assessment effectiveness, absence of robust metrics for societal impact, and lack of a rigorous, unified evaluation framework. This paper systematically introduces social science measurement theory to GenAI evaluation for the first time, proposing a four-layer conceptual framework—construct, operationalization, measurement, and validity—that integrates multidimensional assessment of capabilities, behaviors, and societal impacts. Through construct operationalization, multi-stakeholder participatory design, and systematic validity analysis, the framework enhances theoretical rigor, methodological scalability, and practical inclusivity. Its principal contribution is establishing a principled theoretical interface between machine learning and social science methodologies, fostering cross-disciplinary concept co-development and critical reflection on measurement validity—thereby providing a reusable methodological foundation for responsible AI governance. (149 words)

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Evaluation and Analysis
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as"a tangle of sloppy tests [and] apples-to-oranges comparisons"(Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for evaluating GenAI systems. Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI. This framework has two important implications for designing and evaluating evaluations: First, it can broaden the expertise involved in evaluating GenAI systems by enabling stakeholders with different perspectives to participate in conceptual debates. Second, it brings rigor to both conceptual and operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements.

Problem

• Generative AI
• Effectiveness Evaluation
• Social Impact Assessment

Innovation

• Social Science Theory
• Generative AI Systems
• Comprehensive Evaluation Framework