People often say this as if it were self-evident:<\/p>\n
\u2014 AI can be an excellent executor, but judgment about the result must stay with a human!<\/p>\n
It sounds confident and tidy. And every time I hear it, the same question surfaces: why, exactly, must<\/em>?<\/p>\n <\/p>\n Not \u00abis it convenient?\u00bb \u2014 not \u00abis it what we're used to?\u00bb \u2014 not \u00abdoes it feel uncomfortable to let go?\u00bb \u2014 but this specific, unconditional \u201cmust\u201d. What practical reason makes this obligation necessary? In many cases, it may be an untested assumption rather than a conclusion from practice.<\/p>\n A more precise question is what stands behind the word. When someone says judgment must stay with them, they rarely mean a technical constraint. More often the question is: why can't you, don't you know how to, or won't you offload this part to an LLM?<\/p>\n Three different answers, and none of them are comfortable. \u00abCan't\u00bb means you haven't figured out how it's done. \u00abDon't know how\u00bb \u2014 the same thing, stated more honestly. \u00abWon't\u00bb \u2014 this raises a different issue, because behind \u00abwon't\u00bb is usually where incentives and role protection may be involved.<\/p>\n Yes, delegating judgment can be a nontrivial task. It may require explicit criteria, test cases, and review loops. But the persistence of the resistance \u2014 that's not about difficulty. Some difficult problems are worth formalizing when the payoff is high enough. Persistence is what happens when someone doesn't want<\/em> to solve it, because the solution takes away something pleasant.<\/p>\n There is already a standard pattern for this, because \u00abjudgment can't be delegated\u00bb collapses against a simple fact: LLM evaluation pipelines often use the pattern called \u201cmodel as a judge\u201d.<\/p>\n Model as a judge<\/strong> \u2014 when a second model evaluates the output of the first. One model generates, the other judges. The exact \u00abjudgment about the result\u00bb that was just declared sacred human prerogative \u2014 moved into a separate, documented evaluation step, when the criteria are explicit enough.<\/p>\n This is not limited to research labs. Major AI vendors describe this pattern in their educational materials. It is used in production systems, especially for tasks with checkable criteria, by teams treating evaluation as an engineering problem. While some are explaining why only humans can judge, others have already automated the judging and moved on.<\/p>\n Honest about the boundaries, otherwise the argument becomes overstated.<\/p>\n Yes, technically there are nuances. They depend on what exactly you're sending to judgment.<\/p>\n But between these two extremes many tasks fall between these cases. A range of intermediate cases already trivially implementable in any business. Text, reports, structured data, support responses, decision drafts \u2014 many recurring business tasks where evaluation can often be delegated if the acceptance criteria are explicit.<\/p>\n A more useful boundary is this: it exists, but it moves. It doesn't run between \u00abhuman\u00bb and \u00abmachine.\u00bb It runs between \u00abalready formalized the criterion\u00bb and \u00abhaven't formalized it yet.\u00bb As tooling improves, more criteria become practical to formalize.<\/p>\n The stronger claim is about incentives.<\/p>\n Asserting that a human simply must<\/em> keep judgment to themselves is often too broad. Not because humans shouldn't make decisions \u2014 of course they will. But \u00abmust\u00bb in its universal, unconditional form is no longer about the work. It can function as a way to preserve authority without specifying which part truly requires human judgment.<\/p>\n The issue is this. \u00abJudgment can't be delegated\u00bb isn't an observation about the nature of AI. It's a convenient default assumption for someone who noticed that execution was taken from them, and is rushing to stake out at least one function they are reluctant to delegate. Judgment got assigned as the last line of defense not because it can't be handed off \u2014 but because it is harder to formalize and therefore easier to defend as exclusively human.<\/p>\n The practical test is straightforward.<\/p>\n Stop asking whether a human must<\/em> judge. Ask something else: what exact evaluation criterion am I holding in my head right now \u2014 and can I pull it out?<\/strong> If you can articulate what \u00aba good result\u00bb looks like in your task \u2014 you can start turning it into an evaluation procedure. Then you can choose an implementation: a second model, checks, reference examples, or metrics.<\/p>\n What genuinely needs a human: setting the criterion itself, catching the case that doesn't fit, and deciding when the criterion needs to change. This remains a substantial responsibility. That's the actual level of judgment someone gets paid for. But it's specific work against a moving boundary \u2014 not a universal rule that applies regardless of task type.<\/p>\n The difference between the two positions is the difference between the person who pushes the boundary of what's delegable and the person who sits on their side of it explaining why it can't be pushed. One approach expands what can be delegated; the other treats the current boundary as fixed.<\/p>\n When someone says \u00abjudgment must stay human,\u00bb ask the real question: is this a task-specific requirement, or an assumption that has not been tested?<\/p>\n","protected":false},"excerpt":{"rendered":" The word \u00abmust\u00bb in \u00abAI can execute, but judgment must stay human\u00bb is not a law of nature. It’s a doctrine protecting a comfortable seat.<\/p>\n","protected":false},"author":0,"featured_media":144,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[2,132,131,130,133,129],"class_list":["post-145","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-ai","tag-ai-delivery","tag-delegation","tag-judgment","tag-mindset","tag-model-as-a-judge"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\tWhat the \u00abmust\u00bb is actually protecting<\/h2>\n
Model-as-a-judge \u2014 not frontier science, it's a vendor course<\/h2>\n
Where delegation is easier and where it remains hard<\/h2>\n
\n
So, about that chair<\/h2>\n
How to test whether judgment can be delegated<\/h2>\n