Algorithms now decide who gets a loan, who gets bail, who gets hired, and what medical treatment you receive. Most of these systems are black boxes — even their creators can't explain specific decisions. Should the law require that any AI making decisions about people be fully transparent and auditable?
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Unexplainable AI decisions can't be audited for bias. ProPublica's investigation of the COMPAS recidivism algorithm revealed it was twice as likely to falsely flag Black defendants as high risk. Without transparency, discriminatory systems operate unchecked.
Human decision-makers aren't required to explain their reasoning. A human loan officer can deny your application based on a "gut feeling" with no algorithmic audit trail. Holding AI to a higher standard than humans creates a perverse incentive to keep opaque human processes.
We already regulate outcomes without requiring process transparency. We test drugs for safety without requiring pharmaceutical companies to explain every molecular interaction. Similarly, we can test AI systems for bias and accuracy without demanding they explain every decision.
Requiring full explainability would ban the most powerful AI models. Deep neural networks work precisely because they find patterns too complex for humans to articulate. Mandating interpretability means settling for less accurate systems that make worse decisions for everyone.
