Source-linked AI summary
A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts
Alexandra Chouldechova, Chad Atalla, Solon Barocas, A. Feder Cooper, Emily Corvi, P. Alex Dow, Jean Garcia-Gathright, Nicholas Pangakis, Stefanie Reed, Emily Sheng, Dan Vann, Matthew Vogel, Hannah Washington, Hanna Wallach
TL;DR
GenAI evaluations need valid measurement of capabilities, risks, and impacts, but existing practices are disparate and often under-specify what is measured. The paper extends measurement theory into a shared framework covering concepts, contexts, metrics, and populations, enabling evaluations to be better understood, checked, and compared. The framework advances evaluation toward more formalized and theoretically grounded practice, while leaving guidance for carrying out measurements to future work.
Problem
GenAI evaluations require valid measurement, yet disparate practices can under-specify concepts, amounts, populations, and instances.
Method
The paper extends Adcock and Collier’s framework by systematizing, operationalizing, and applying concepts, amounts, populations, and instances within a shared measurement framework.
Results
The framework places disparate GenAI evaluations on a common footing, enabling them to be better understood, interrogated for reliability and validity, and meaningfully compared.
Takeaways & Limitations
The framework is intended to move GenAI evaluation toward more formalized and theoretically grounded processes—a science of GenAI evaluations.
Takeaways & Limitations
The framework does not guide how to accomplish measurement tasks, formulate them, interpret measurements, or decide what they should inform.
Abstract
from arXiv · showhide
The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems. We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework, grounded in measurement theory from the social sciences, extends the work of Adcock & Collier (2001) in which the authors formalized valid measurement of concepts in political science via three processes: systematizing background concepts, operationalizing systematized concepts via annotation procedures, and applying those procedures to instances. We argue that valid measurement of GenAI systems' capabilities, risks, and impacts, further requires systematizing, operationalizing, and applying not only the entailed concepts, but also the contexts of interest and the metrics used. This involves both descriptive reasoning about particular instances and inferential reasoning about underlying populations, which is the purview of statistics. By placing many disparate-seeming GenAI evaluation practices on a common footing, our framework enables individual evaluations to be better understood, interrogated for reliability and validity, and meaningfully compared. This is an important step in advancing GenAI evaluation practices toward more formalized and theoretically grounded processes -- i.e., toward a science of GenAI evaluations.
1 Introduction
The paper frames GenAI capability, risk, and impact evaluations as measurement tasks and connects them to validity concerns from measurement theory. It extends prior work by emphasizing precise concepts, procedures, and applications.
- GenAI evaluations can be framed as measuring an amount of a concept in instances drawn from a population.Examples include average mathematical reasoning performance on standardized questions and stereotyping prevalence in deployed system outputs.
- The paper uses this measurement-validity perspective to motivate a more systematic foundation for evaluating GenAI systems.
- Adcock and Collier formalized valid concept measurement as progressing from background concepts to systematized concepts, annotation procedures, and applied scores or labels.Validity concerns arise when these levels fail to align, such as when annotation procedures omit relevant dimensions of a concept.
2 Framework Overview
The framework extends concept-focused measurement theory to amounts, populations, and instances, combining descriptive reasoning about observations with statistical inference about populations. It supports iterative refinement across measurement components.
- Framework Overview: Valid GenAI measurement must systematize, operationalize, and apply not only concepts but also amounts, populations, and instances.The framework therefore treats both descriptive reasoning about particular instances and inferential reasoning about underlying populations as essential.
- Framework Overview: The authors extend Adcock and Collier’s framework and refine it through examples spanning GenAI and non-generative AI capabilities, risks, and impacts.
- Framework Overview: Later findings can trigger revisions within or across framework columns, such as changing sampling designs or annotation procedures when measurement variance is too high.
- Framework Overview: Representing instances, defining target parameters, and selecting estimators determine whether measurements match the intended deployment population and support appropriate statistical inference.For example, adversarial red-teaming samples can overestimate stereotyping prevalence under typical post-deployment use.
3 Limitations and Future Work
The framework structures what should be specified in GenAI measurement tasks but does not explain how to carry them out. Its broader scope also excludes task formulation, interpretation, and decision guidance.
- Limitations and Future Work: The framework offers no guidance on how to accomplish the measurement tasks it organizes.The authors identify this as an important direction for future work.
- Limitations and Future Work: It does not help formulate measurement tasks or determine how resulting measurements should be interpreted or used in decisions.The authors note that revision mechanisms do not resolve the full complexity of deciding what, where, and how to measure.
- Limitations and Future Work: Figure 1 represents measurement tasks through amounts, concepts, instances, and populations, formalized by systematization, operationalization, and application.
4 Conclusion
The framework places disparate GenAI evaluations on a common footing so they can be examined and compared more systematically. It is intended to advance evaluation toward formalized, theoretically grounded practice.
- Conclusion: The framework enables evaluations to be better understood, checked for reliability and validity, and meaningfully compared.It supports identifying validity concerns, remedying them, and locating where different measurement tasks diverge.
- Conclusion: It is intended as a shared standard that helps move GenAI evaluation from disparate, ad hoc practices toward a science of GenAI evaluations.
- Conclusion: Figure 2 illustrates a high-level hypothetical ChatSearch evaluation, while noting that complete instantiations require substantial additional specification.