Who is ultimately responsible for an AI system?s decisions?
Accountability separates the wishers in life from the action-takers.
How It Works:
Accountability frameworks assign clear roles to data stewards, model owners, and business sponsors with documented handoffs and decision logs.
Key Benefits:
- Clear ownership: Everyone knows their domain of responsibility.
- Audit readiness: Traceable records simplify reviews.
- Faster issue resolution: Pinpointing errors becomes straightforward.
Real-World Use Cases:
- Financial services: Assigning model-owner duties in loan approvals.
- Healthcare: Defining clinicians? and data scientists? roles in diagnostic tools.
FAQs
What's an "accountability matrix"?
How often should roles be reviewed?
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