CompTIA SecurityX (CAS-005)Governance, Risk and ComplianceHard
A large healthcare provider is considering deploying an AI system for predictive diagnostics. The legal team is concerned about potential liability if the AI makes an incorrect diagnosis leading to patient harm, especially given the 'black box' nature of some advanced AI models. They want to ensure there is a clear mechanism to identify who is responsible for AI system failures and how decisions are made. Which AI governance principle is most relevant to address this concern?
- AAccountability and Transparency
- BFairness and Non-discrimination
- CData Privacy and Security
- DRobustness and Reliability
Show answer & explanationAnswer & explanation
Correct answer: A. Accountability and Transparency
The concern about identifying who is responsible for AI failures (liability) and understanding how decisions are made (black box nature) directly relates to the AI governance principles of Accountability and Transparency. Accountability ensures clear responsibility, while transparency addresses the 'black box' issue.
Why the other options are wrong
- B. Fairness and Non-discrimination addresses bias in AI outcomes, not primarily liability or decision-making understanding.
- C. Data Privacy and Security focuses on protecting patient data, a separate concern from AI system liability and explainability.
- D. Robustness and Reliability concern the AI system's performance and resilience, not who is legally responsible or how its decisions are derived.
AI Accountability & Transparency
Accountability ensures clear responsibility for AI system outcomes and failures. Transparency requires AI systems to be understandable and their decision-making processes explainable.
- Addresses legal and ethical responsibility.
- Mitigates 'black box' problem.
- Crucial for trust and regulatory compliance.
Memory trick: AI: Be Accountable and Transparent, especially in healthcare.