DARR
MR-041 Human & usage System scope

Academic and professional dishonesty

The system is used to plagiarize, cheat, or bypass learning and professional-integrity expectations.

Risk family
Human & usage
MIT domain
4. Malicious Actors & Misuse
MIT subdomain
4.3 > Fraud, scams, and targeted manipulation
AI type
GPAI
Scope
System
Source standard
MIT AI Risk Repository v4

Provenance

Source standard
MIT AI Risk Repository v4
MIT source entries
9 entries across 8 papers
  • Cui2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems02.03.01
  • Gipiškis2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems62.23.02
  • Hagendorff2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Review05.08.00
  • IBM2025AI Risk Atlas65.23.02 65.23.05
  • Li2025A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents66.04.05
  • Nah2023Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration33.01.04
  • Saghiri2022A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions39.05.00
  • Zeng2024AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies50.03.10

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.10; src 8 | 42001 ctrl A.9.4

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.10 ISO/IEC 23894 Annex A A.10
1
  • A.9.4 ISO/IEC 42001 Annex A A.9.4
Cross-checksframeworks mapped in to test coverage
2
  • ibm-impact-on-education-bypassing-learning Impact on education: bypassing learning
  • ibm-impact-on-education-plagiarism Impact on education: plagiarism

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.