Inaccuracy and poor predictive performance
The system fails to perform its intended task accurately or helpfully, producing erroneous, low-quality, or generic and homogenized results ('AI slop') that can erode content distinctiveness and organizational credibility, as distinct from fabricated content, brittleness to unusual inputs, and performance drift over time.
- Risk family
- Model & system behaviour
- MIT domain
- 7. AI System Safety, Failures, & Limitations
- MIT subdomain
- 7.3 > Lack of capability or robustness
- AI type
- GPAI, Classical_ML, Agentic
- Scope
- System
- Source standard
- MIT AI Risk Repository v4
Provenance
25 source framework citation keys
Framework crosswalk
Every framework item mapped to this risk. Items marked partial overlap only in part; definitions appear on hover where the source licence permits.
- A.4 ISO/IEC 23894 Annex A A.4
- A.9 ISO/IEC 23894 Annex A A.9
- A.6.2.4 ISO/IEC 42001 Annex A A.6.2.4
- A.6.2.6 ISO/IEC 42001 Annex A A.6.2.6
- Art. 15
- ibm-poor-model-accuracy Poor model accuracy
More in Model & system behaviour
Part of the Deployer AI Risk Register, an open-source resource developed by MindXO. Version 1.0, 3 July 2026. Derived from the MIT AI Risk Repository (V4, December 2025) under CC BY 4.0; an independent derivative work, not endorsed by or affiliated with MIT. Sub-risk decomposition references MITRE ATLAS™ v5.6.0 (© 2021-2026 The MITRE Corporation, reproduced and distributed with permission). ISO/IEC and EU AI Act references are by number only. License: CC BY 4.0. Full attribution and licensing.