THE real test of AI is not what it knows, but how it judges. Now what happens when an AI system is given better evidence and still reaches the same unfair conclusion? That question has become more urgent as AI systems increasingly stand between information and judgement. We tend to assume that if a model can search reliable documents before answering, its decisions will improve. But this confidence may be misplaced.

Most people understand the basic appeal of retrieval-based AI. Instead of asking a language model to rely only on what it learned during training, we first give it relevant material. A system judging a country’s climate record, for instance, can be shown official climate pledges and international agreements. The expectation seems reasonable: better evidence should lead to better judgement.

Yet judgement is not the same as fact-finding. A model can locate a date or a treaty clause with precision. But deciding whether a country has acted responsibly on climate change requires something more. The model must interpret evidence, weigh it against an external standard and revise whatever assumptions it carries. That last step is where the real difficulty appears.

Consider climate governance. Large language models........

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