More than Marketing? On the Information Value of AI Benchmarks for Practitioners (2412.05520v1)

Abstract: Public AI benchmark results are widely broadcast by model developers as indicators of model quality within a growing and competitive market. However, these advertised scores do not necessarily reflect the traits of interest to those who will ultimately apply AI models. In this paper, we seek to understand if and how AI benchmarks are used to inform decision-making. Based on the analyses of interviews with 19 individuals who have used, or decided against using, benchmarks in their day-to-day work, we find that across these settings, participants use benchmarks as a signal of relative performance difference between models. However, whether this signal was considered a definitive sign of model superiority, sufficient for downstream decisions, varied. In academia, public benchmarks were generally viewed as suitable measures for capturing research progress. By contrast, in both product and policy, benchmarks -- even those developed internally for specific tasks -- were often found to be inadequate for informing substantive decisions. Of the benchmarks deemed unsatisfactory, respondents reported that their goals were neither well-defined nor reflective of real-world use. Based on the study results, we conclude that effective benchmarks should provide meaningful, real-world evaluations, incorporate domain expertise, and maintain transparency in scope and goals. They must capture diverse, task-relevant capabilities, be challenging enough to avoid quick saturation, and account for trade-offs in model performance rather than relying on a single score. Additionally, proprietary data collection and contamination prevention are critical for producing reliable and actionable results. By adhering to these criteria, benchmarks can move beyond mere marketing tricks into robust evaluative frameworks.

• The paper finds that practitioners primarily use AI benchmarks to gauge relative performance progression rather than as definitive indicators of a model's adequacy for specific applications.
• A notable gap exists between the capabilities measured by standard benchmarks and the contextual needs of real-world deployment, prompting the creation of proprietary or ad-hoc evaluations.
• Future AI benchmarks need to incorporate more contextual relevance and feedback loops from practitioners and stakeholders to better support informed real-world decision-making.

An Analysis of the Role and Impact of AI Benchmarks in Practitioners' Decision-Making

The paper "More than Marketing? On the Information Value of AI Benchmarks for Practitioners" authored by Amelia Hardy et al., embarks on an exploratory investigation into the significance of AI benchmarks within the applied decision-making processes of academia, industry, and policy. The research adopts a qualitative approach, relying on semi-structured interviews with 19 practitioners covering dimensions such as academia, industry product development, and policy, thus providing a composite view into the utility and perception of AI benchmarks in these spheres.

Summary of Findings

The paper reveals that AI benchmarks predominantly serve as markers of relative performance progression rather than definitive indicators of a model's adequacy for specific applications. Practitioners across various domains use benchmarks as a reference for gauging improvements over predecessors, with low scores intervening as deterrents against deployment. However, a high score, especially in industry and policy settings, is not perceived as sufficient validation for deployment due to the benchmarks' limited portrayal of real-world requisites. This renders benchmarks in academic contexts as purported measures of research progression, largely divorced from real-world applications.

Practically, benchmarks are often scrutinized for not reflecting the elaborate nuances and challenges transpiring in deployment scenarios. There exists a notable gap between capabilities evaluated by standard benchmarks and what is contextually consequential in authentic workflows. In industry and policy, proprietary or ad-hoc benchmarks are increasingly devised to bridge this gap, signaling a pronounced quest for specificity and relevance in evaluation metrics.

Practical Implications and Theoretical Considerations

The paper documents a critical conception wherein benchmarks are developed without substantial alignment with the operational ecosystem in which the AI models will reside. This dichotomy between constructed tasks and real-world applications challenges the standardization ethos of benchmarks. Benchmark developers are encouraged to incorporate more contextual relevance and integrate feedback loops involving domain and community stakeholders to frame benchmarks that resonate with real-world use cases.

Theoretically, this research underscores a need to map AI benchmark development against the standards of technology adoption frameworks such as UTAUT. The underperformance observed in benchmarks' practicality could be tethered to unmet performance expectancy—benchmarks' perceived failure in furnishing actionable insights necessary for real-world decisions. Consequently, an interdisciplinary approach combining human-centered perspectives in design and rigorous feedback mechanisms in evaluation may enhance the empirical alignment and adoption of benchmarks.

Future Directions in AI Benchmarking

Future developments would benefit from a refined perspective that sees benchmarks not as end-all-be-alls but as a component within a broader evaluative curriculum, involving human consults and contextual examinations. As AI systems become more intertwined with high-stakes decisions, the paper encourages the creation of benchmarks that sensibly track dimensions critical to an AI system's operational integrity and societal efficacy. Achieving this will necessitate collaborative endeavors among AI practitioners, domain experts, policymakers, and end-users.

In conclusion, while the paper recognizes the conventional role benchmarks have played, it emphatically points out the need for a paradigmatic shift towards benchmarks that truly measure for understanding real-world capability, ensuring practitioners are equipped with comprehensive insights for informed decision-making in deploying AI models. This paper thus lays a foundational basis for reconceptualizing how benchmarks can evolve to better support the burgeoning needs of AI integration in societal frameworks.