MIT AI Risk Repository · Risk Sub-Category · 37.02.04
Attributing the responsibility for AI's failures
Category: Human-AI interaction
Description
"This section, constituting almost 8% of the articles, addresses the implications arising from AI acting and learning without direct human supervision, encompassing two main issues: a responsibility gap and AI's moral status."
From What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review (Giarmoleo2024), 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
- General Evaluations (Difficulty of identification and measurement of capabilities)
- Model outputs inconsistent with chain-of-thought reasoning
- Lack of understanding of in-context learning in language models
- Model Evaluations (Interpretability/Explainability)
- Lack of transparency and interpretability
- Opacity (the black box problem)
- Transparency - Explainability