MIT AI Risk Repository · Risk Sub-Category · 65.17.04
Unreliable source attribution
Category: Output risks (Explainability)
Description
"Source attribution is the AI system's ability to describe from what training data it generated a portion or all its output. Since current techniques are based on approximations, these attributions might be incorrect."
From AI Risk Atlas (IBM2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
Subdomain definition: Challenges in understanding or explaining the decision-making processes of AI systems, which can lead to mistrust, difficulty in enforcing compliance standards or holding relevant actors accountable for harms, and the inability to identify and correct errors.
Real-world incidents in this subdomain
- Uber Launched Opaque Algorithm That Changes Drivers' Payments in the US
- Israeli Tax Authority Reportedly Used an Opaque Automated System to Issue a Fine, Declining to Explain or Disclose the Underlying Calculation
- Uber Allegedly Wrongfully Accused Drivers of Fraud via Automated Systems
- Houston ISD's EVAAS Teacher-Evaluation System Reportedly Put Teachers' Jobs at Risk Through Unverifiable Scores
- Dutch City Court Defended Home Value Generated by Black-Box Algorithm
How other frameworks describe this risk
- Intelligibility
- Attributing the responsibility for AI's failures
- Model Evaluations (Interpretability/Explainability)
- Model outputs inconsistent with chain-of-thought reasoning
- General Evaluations (Difficulty of identification and measurement of capabilities)
- Lack of understanding of in-context learning in language models
- Lack of transparency and interpretability
- Opacity (the black box problem)