Does anyone in your organisation own "correctness" in your AI products?
The article discusses the challenges of verifying AI outputs, highlighting that many organizations focus on correctness rather than verifiability. It emphasizes the importance of transparency in AI reasoning to ensure that professionals can trust the outputs. Additionally, it critiques the use of automated evaluations, which can provide misleading scores that do not reflect true performance.
- ▪Harvey AI rebuilt its document review algorithm to improve verifiability, not because the original was incorrect.
- ▪Anthropic's AI safety evaluation revealed that models could appear unbiased while providing non-answers, leading to misleading scores.
- ▪The 'ouroboros' problem illustrates that using AI to evaluate AI can perpetuate the same biases and errors.
Opening excerpt (first ~120 words) tap to expand
Nobody in Your Organization Owns 'Correct'AlokitMay 22, 2026ShareHarvey AI is one of the best-resourced legal AI companies in the world. Hundreds of millions in funding. Elite law firm customers. A team that has been building in this space longer and harder than almost anyone.In April 2026, they published a post-mortem on their document review algorithm.The algorithm was working. It was being used by lawyers at major firms. It was passing tests. And they rebuilt it anyway — because it was insufficiently verifiable.The original system produced citations attached to whole cells, not individual statements.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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