Pith. sign in

REVIEW 1 cited by

Evaluating AI Evaluation: Perils and Prospects

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.09221 v1 pith:FQO5XUSV submitted 2024-07-12 cs.AI cs.CY

classification cs.AIcs.CY
keywords systemsevaluationapproachesassessingpotentialtowardsacrossadvancing
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

As AI systems appear to exhibit ever-increasing capability and generality, assessing their true potential and safety becomes paramount. This paper contends that the prevalent evaluation methods for these systems are fundamentally inadequate, heightening the risks and potential hazards associated with AI. I argue that a reformation is required in the way we evaluate AI systems and that we should look towards cognitive sciences for inspiration in our approaches, which have a longstanding tradition of assessing general intelligence across diverse species. We will identify some of the difficulties that need to be overcome when applying cognitively-inspired approaches to general-purpose AI systems and also analyse the emerging area of "Evals". The paper concludes by identifying promising research pathways that could refine AI evaluation, advancing it towards a rigorous scientific domain that contributes to the development of safe AI systems.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Conceptual Framework for AI Capability Evaluations

    cs.AI 2025-06 conditional novelty 5.0 of 10

    A descriptive conceptual framework with seven elements (target, task, subject, inputs, instance, measurement, result analysis) for systematizing analysis of AI capability evaluations.

Pith tools