Enterprise AI Analysis
Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge
The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, 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 systems. This framework has two important implications: 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 validity.
Authors: Hanna Wallach, Meera Desai, A. Feder Cooper, Angelina Wang, Chad Atalla, Solon Barocas, Su Lin Blodgett, Alexandra Chouldechova, Emily Corvi, P. Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Nicholas Pangakis, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs
Key Takeaways for Decision Makers
Our analysis identifies critical insights for enterprise leaders navigating Generative AI, focusing on strategic evaluation and ethical implementation.
0
GenAI Evaluations Lack Rigor
0
Level Framework Adopted
0
Improvement in Decision Accuracy
0
Expanded Expertise Engagement
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Framework Overview
Validity Interrogation
Real-World Examples
Understanding the four-level measurement framework (Background Concept, Systematized Concept, Measurement Instruments, Measurements) and its processes: systematization, operationalization, application, and interrogation.
GenAI Measurement Process Flow
Background Concept
→
Systematized Concept
→
Measurement Instruments
→
Measurements
| Aspect | Traditional ML Approach | Social Science Approach |
|---|---|---|
| Concept Definition | Often implicit, high-level, conflated with instruments. | Explicit, systematized, subject to conceptual debates. |
| Validity Interrogation | Limited, often ad-hoc, mostly focused on benchmarks. | Rigorous, multi-faceted (7+ lenses), context-dependent. |
| Stakeholder Involvement | Primarily ML experts. | Broad, interdisciplinary engagement in conceptual debates. |
| Measurement Focus | Operational aspects, direct performance. | Both conceptual clarity and operational validity. |
Diving into the seven lenses of validity (face, content, convergent, discriminant, predictive, hypothesis, and consequential) and their role in ensuring rigorous GenAI evaluations.
7+
Lenses of Validity Essential for Rigorous GenAI Evaluation
Illustrative case studies on stereotyping and memorization in LLMs, demonstrating the practical application of the framework and validity lenses.
Case Study: Stereotyping in LLMs
The paper highlights how abstract concepts like 'stereotyping' are currently ill-defined in many ML benchmarks (e.g., StereoSet, CrowS-Pairs). A social science approach would involve systematizing the concept with explicit definitions (e.g., 'text that communicates fixed, over-generalized beliefs'), connecting it to observable linguistic patterns, and critically interrogating its validity with diverse stakeholders, avoiding reliance on implicit crowd-worker interpretations.
Case Study: Memorization in GenAI
Measuring GenAI memorization is a complex task with significant privacy and copyright implications. The framework clarifies the distinction between 'regurgitation' and 'extraction' and emphasizes the need for a systematized concept of memorization. This involves decisions on what constitutes a 'piece of training data,' 'exact' vs. 'near-exact' copies, and how to rigorously validate measurement instruments to ensure they accurately reflect the underlying concept, not just happenstance generation.
Calculate Your Potential AI Impact
Estimate the efficiency gains and cost savings your enterprise could realize by implementing robust GenAI evaluation and deployment strategies.
Your Path to Rigorous GenAI Evaluation
We guide enterprises through a structured roadmap to adopt social science-backed measurement for GenAI.
Phase 1: Conceptual Alignment Workshop
Facilitate stakeholder workshops to systematize concepts of interest (e.g., safety, bias, memorization), explicitly defining what needs to be measured and why, drawing on diverse expertise.
Phase 2: Instrument Development & Validation
Design or adapt measurement instruments (benchmarks, guidelines, LLM-as-a-judge setups) grounded in the systematized concepts, followed by rigorous validity interrogation using social science lenses.
Phase 3: Pilot Implementation & Iteration
Conduct pilot evaluations within enterprise contexts, gather initial measurements, and use the interrogation process to iteratively refine both conceptual definitions and measurement instruments.
Phase 4: Scaled Integration & Continuous Monitoring
Integrate validated measurement practices into your GenAI lifecycle, establish continuous monitoring, and ensure ongoing engagement with relevant disciplines for long-term validity and impact.
Ready to Transform Your AI Evaluation?
Book a free 30-minute strategy session to discuss how our social science-backed measurement framework can bring rigor and clarity to your enterprise's GenAI systems.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat