MIT AI Risk Repository · Risk Category · 12.04.00
Explainability & Transparency
Description
"The feasibility of understanding and interpreting an AI system's decisions and actions, and the openness of the developer about the data used, algorithms employed, and decisions made. Lack of these elements can create risks of misuse, misinterpretation, and lack of accountability."
From AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures (Sherman2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
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)
- Lack of understanding of in-context learning in language models
- Model outputs inconsistent with chain-of-thought reasoning
- General Evaluations (Difficulty of identification and measurement of capabilities)
- Lack of transparency and interpretability
- Opacity (the black box problem)