Public trust in AI should be built through clear and understandable processes rather than assumptions of perfect accuracy. The argument draws a parallel with the historical credibility of US courts, where trust has rested on hearing both sides, explaining decisions, and allowing rulings to be challenged or reversed. The comparison suggests AI systems seeking wider public acceptance may need similar procedural safeguards. Transparent reasoning, the opportunity to contest outcomes, and mechanisms for review are presented as core elements for building legitimacy.