DARR
MR-059 Model & system behaviour Both scope

Poor data quality and representativeness

Training/operational data is inaccurate, unrepresentative, mislabeled, contaminated, or poorly curated, undermining reliability.

Risk family
Model & system behaviour
MIT domain
7. AI System Safety, Failures, & Limitations
MIT subdomain
X.1 > Excluded
AI type
GPAI, Classical_ML
Scope
Both
Source standard
MIT AI Risk Repository v4

Provenance

Source standard
MIT AI Risk Repository v4
MIT source entries
27 entries across 5 papers
  • AIVerify2023Summary Report: Binary Classification Model for Credit Risk26.08.00
  • Gipiškis2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems62.04.00 62.04.03 62.04.04 62.04.05 62.04.06 62.14.03
  • IBM2025AI Risk Atlas65.01.00 65.02.00 65.03.00 65.03.02 65.04.00 65.06.00 65.06.01 65.06.02 65.17.01 65.17.02 65.17.04
  • Schnitzer2024AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks59.07.00 59.08.00 59.11.00 59.13.00 59.14.00 59.15.00 59.17.00 59.22.00
  • Teixeira2022An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance42.13.00

Ev IDs of the entries consolidated into this risk in the MIT AI Risk Repository (V4); the source sheet row appears on hover.

ISO/IEC references
23894 obj A.4; src 6; mech B.5 | 42001 ctrl A.7.4, A.7.6
EU AI Act articles
Art. 10 | Art. 26(4)

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.

Sourcesframeworks that contributed to the register
1
  • A.4 ISO/IEC 23894 Annex A A.4
2
  • A.7.4 ISO/IEC 42001 Annex A A.7.4
  • A.7.6 ISO/IEC 42001 Annex A A.7.6
2
  • Art. 10
  • Art. 26(4)
Cross-checksframeworks mapped in to test coverage
5
  • ibm-data-contamination Data contamination
  • ibm-improper-data-curation Improper data curation
  • ibm-introduce-data-bias Introduce data bias partial
  • ibm-temporal-gap Temporal gap partial
  • ibm-unrepresentative-data Unrepresentative data

Part of the Deployer AI Risk Register, an open-source resource powered 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.