Consensus Without Consequence, the Collapse of AI Accountability
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Consensus Without Consequence, the Collapse of AI Accountability
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The global agreement on AI ethics (fairness, transparency, accountability) has not translated into enforcement, creating a widening gap between principles and practice.
Reviews of hundreds of guidelines show strong convergence on stated values, but major divergence on interpretation and implementation, enabling “ethics washing,” illustrated by Google’s 2020 firing of Timnit Gebru and later Margaret Mitchell.
Industry adoption of generative AI is rapid while governance lags, especially as agentic systems spread. Regulatory responses are uneven: the EU AI Act phases enforcement through 2027, while the US is fragmented and contested between federal policy and state laws like Colorado and NYC rules. Real-world harms persist in hiring, housing, and biometric surveillance (Workday, SafeRent, Clearview), with slow legal remedies and documented bias in studies.
Audits are costly, time-limited, and structurally insufficient, and there is critical need for anticipatory, well-resourced, iterative governance with meaningful penalties and broader transparency.