Accountability: Which responsible-AI principle does this commitment express?
Ensuring an identifiable party is answerable for AI outcomes and their remediation expresses the accountability principle.
The question
An organization commits that when its AI makes a mistake affecting a customer, a specific person or team can be identified as answerable for the outcome and its remediation. Which responsible-AI principle does this commitment express?
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- Transparency, because stakeholders are given clear and accessible information about how the AI system functions and reaches its results.Plausible, since accountability and transparency pair often, but disclosure of how the system works is distinct from being answerable for outcomes.
- Fairness, because the system is designed to treat similar individuals equitably and to avoid unjustified discriminatory effects on them.Almost right as a core principle, but fairness concerns equitable treatment, not who answers when something goes wrong.
- Safety and reliability, because the system is built to perform dependably and to minimize the likelihood of causing unintended harm.Plausible, but safety and reliability concern dependable performance, not the assignment of answerability for outcomes.
- Accountability, because a clearly identifiable party remains answerable for the AI system's outcomes and for correcting resulting harm. ✓Correct. Ensuring an identifiable party answers for outcomes and remediation is the accountability principle.
The trap
Treating transparency and accountability as interchangeable, when one is about disclosure and the other about answerability. How to remember it
Ensuring an identifiable party is answerable for AI outcomes and their remediation expresses the accountability principle.
How many of these would you get right?
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