AI Evaluation's Credibility GapDemands Granular Data Standards
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- ★Current AI evaluation paradigms sufferfrom systemic validity failures stemming from unjustified design choices andmisaligned metrics.
- ★Aggregate benchmark scores obscureitem-level data, preventing the diagnostic analysis required to identifywhere and why models fail.
- ★Open repositorieslike OpenEval are essential for providing the granulardata needed to establish genuine validity evidence prior to deployment.
AIsystems now steer critical decisions in healthcare, finance,and infrastructure based on benchmark scores that may fundamentally fail tomeasure what they claim. A rigorous position paper detailshow current evaluation paradigms exhibit systemic validity failures, stemming from unjustified designchoices and misaligned metrics that remain intractablewithout finer-grained analysis. The core problem is architectural: most benchmarks report aggregate scores while hiding the item-level data that would reveal precisely where and why modelscollapse.
Without access to individual test items and their performancepatterns, researchers cannot conduct the principled diagnostic analysisneeded to establish genuine validity evidence. This deficit carries severeconsequences as generative AI deployment decisions increasinglyhinge on these opaque evaluations. The authors contend that computerscience has borrowed evaluation frameworks from other disciplines without adoptingthe psychometric rigor that underpins valid measurement inthose fields. Item-level analysis would enable fine-grained diagnostics: identifying whether failures cluster on specific reasoningtypes, demographic groups, or edge cases that aggregatescores completely obscure.
The authors frame this as essential infrastructurefor a rigorous science of AI evaluation, not merely a technical convenience.The critique extends deeply into transparency. Current benchmarks oftenlack documented rationale for design choices, making it impossibleto assess whether metrics align with the actual constructs theypurport to measure.
Aggregate benchmark scores concealsystemic model weaknesses in critical infrastructure
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When a model achieves a seemingly high score, theaggregate metric masks systemic blind spots that could prove catastrophic inspecialized domains.
A model might excel at straightforward retrievalwhile failing entirely on complex multi-step reasoning, yetboth capabilities remain hidden within the same flattened percentage. Theproposed solution demands open repositories like OpenEval to provide thegranular data required to establish genuine validity evidence priorto deployment. Such infrastructure would shift the evaluation paradigm fromsuperficial ranking to rigorous diagnostic assessment, enabling developers to tracespecific failure modes back to their architectural or data-drivenorigins.
As AI benchmarks continue to mislead us, the fieldmust abandon the pretense that a single composite score cancertify model competence for high-stakes applications. Validity requires transparency at the item level, documented designrationale, and metrics explicitly aligned with the constructs beingmeasured. Without this psychometric foundation, AI evaluation remainsa fragile veneer over deep structural ignorance, riskingdeployment of systems whose true capabilities remain dangerously uncharacterized.