When an AI-assisted decision goes wrong — who owns it? Not in theory. Not across a committee. Who, by name, is accountable? This is the question most organizations have not answered — and regulators, boards, and affected individuals are starting to ask it loudly. In this episode, Dr. Marilyn Carroll separates responsibility, accountability, and liability — three terms that get conflated in ways that create dangerous gaps — and lays out what it looks like to design accountability into AI workflows before deployment rather than assign it after failure. Boards are now personally liable for AI failures. The organizations that built accountability structures early are ahead. The ones that didn't are catching up under pressure.
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