Benchmark Inaccuracy (Benchmarks may not accurately evaluate capabilities)

Record summary

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Techniques0Attack methods connected to this risk.

Mitigations0Defenses that may help with related attacks.

Domain6. Socioeconomic and EnvironmentalThe broad risk area this belongs to.

Risk profile

How this risk is described and categorized.

"Benchmarks of AI systems can both underestimate and overestimate the capa- bilities of those AI systems. Underestimates can happen if an evaluation is not comprehensive enough, if the benchmark is saturated by existing models, or if the capabilities in question depend on a complicated setup, such as realistic computer programming tasks. Overestimates of capabilities can occur if an AI system is trained or fine-tuned on the contents of the benchmark, leading to overfitting."

Domain6. Socioeconomic and Environmental

Subdomain6.5 > Governance failure

Entity1 - Human

Intent2 - Unintentional

Timing1 - Pre-deployment

CategoryModel Evaluations

SubcategoryBenchmark Inaccuracy (Benchmarks may not accurately evaluate capabilities)

Related techniques

Attack methods connected to this risk.

No linked attack methods. No AI attack method is connected to this risk in the current data.

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

Research source for this risk, when available.

Included resource

Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems

AuthorsGipiškis et al.Year2024TypeJournal Article

DOIhttps://doi.org/10.48550/arXiv.2410.23472 URLhttps://arxiv.org/abs/2410.23472

Original source

MIT AI Risk Repository

Open the public repository used for AI risk records and taxonomy fields.

Repositoryhttps://airisk.mit.edu/