MIT AI Risk Repository · Risk Category · 01.02.00

Type 2: Bigger than expected

Description

Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

From TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Causal entity
AI

Subdomain definition: AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.

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How other frameworks describe this risk

Other entries from Critch2023