Who Answers for What AI Does?
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Who Answers for What AI Does?
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When AI influences a decision, who is actually accountable for the outcome?
In this episode of Answerable AI, J.M. Wofford examines the gap between involvement and accountability in AI governance. An AI system may involve vendors, technology teams, data owners, business leaders, legal, compliance, and human decision-makers, yet an organization can still struggle to answer a basic question: Who answers when the system produces an unexpected or unacceptable result?
This episode explores:
• Why distributed responsibility can become distributed ambiguity
• The difference between explainable AI and answerable AI
• Why “human in the loop” does not automatically create meaningful oversight
• Why accountability must come with real decision authority
• What organizations still own when AI is supplied by a third-party vendor
• Why governance requires current evidence, not just pre-deployment approvals
• The questions every organization should be able to answer about an AI-enabled process
Answerable AI is a Blue Narwhal podcast about AI governance, measurement, risk, compliance, and organizational accountability.
Hosted by JM Wofford
Better questions. Safer systems. Brighter tomorrows.