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Understanding how automation changes work — task by task, with the evidence shown and the uncertainty admitted.

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On this pageWhich technologiesHow it got hereMethod and sources
Occupations›Judge / magistrate›How we know

Judge / magistrate — how we know

The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.

Assessed
2026-10-01
With evidence
5/5
Verified events
10

Which technologies matter here#

Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.

Cognitive automation
Hearing cases and running proceedingsResearching the lawDrafting judgments and ordersDeciding the case
Process & self-service
Case management and administration

How it got here#

The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.

Reconstructed · platform inferenceEstimated today for each past checkpoint — not measured at the time. 10 → 24.
1007550250
Employment of judges and hearing officers will grow 2 percent from 2025 to 2035, limited by public budgets, the US Bureau of Labor Statistics estimated, without mentioning AIJudges must always read the underlying documents, and AI is a poor way to research new information they cannot verify, the judiciary of England and Wales told its judgesAny court that does not prohibit generative AI must adopt a use policy for staff and judicial officers, California Rules of Court rule 10.430 saysA private Copilot Chat tool was made available on judicial office holders' devices, the judiciary of England and Wales said in April 2025AI that assists a judicial authority in researching and applying the law is high-risk, with obligations applying from December 2027, under the EU Artificial Intelligence ActLeading AI legal research tools from LexisNexis and Thomson Reuters hallucinated between 17% and 33% of the time, a Stanford study foundGeneral-purpose language models were wrong about random federal court cases between 58% and 88% of the time, researchers foundUsing generative AI chatbots for legal analysis is not recommended because they generate text by probability, New Zealand's courts told judges and judicial staffHowever far technology develops, AI must not take the place of a judge's ruling, though it may assist drafting and case administration, China's Supreme People's Court said123456789not assessed
2022 H22024 H2Now

—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed

● 9 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.

Starts at 10 because legal databases and electronic case management were routine before this chart begins, while hearing, drafting and deciding were done by judges. It climbs with language models that can summarise and draft and with judiciaries providing approved tools, and stays low because every judiciary here keeps the decision with the judge, requires judges to read the evidence themselves, and warns that legal research tools hallucinate.

12022 H210General-purpose text generation reaches the public. Before this point, exposure came from automation that was already deployed — OCR, RPA, machine vision, self-checkout, dispatch algorithms. ChatGPT research preview (2022-11-30) ↗
22023 H111A general model that passes professional exams. First-draft quality crosses the threshold where professional work starts using it. GPT-4 (2023-03-14) ↗
32023 H212Vision input, long context and tool calling. Models can be pointed at documents and connected to systems, which is what moves process work rather than writing work. GPT-4 Turbo:128k 上下文、视觉、工具调用(DevDay) (2023-11-06) ↗
42024 H114The same capability gets much cheaper and faster. Nothing new becomes possible; a lot becomes affordable at volume, which is when deployment decisions change.
52024 H217Reasoning models that work through multi-step problems, and the first models that operate a computer by looking at the screen. The second one is what reaches software-operating jobs. OpenAI o1(推理);同期 Claude 的 computer use 进入公测 (2024-09-12) ↗
62025 H119Agents begin operating real software end to end rather than producing text for a person to paste. This is also when the first public reversals appear — organisations that automated and partly undid it. Claude 3.7 Sonnet 与 Claude Code:混合推理 + 命令行编码代理 (2025-02-24) ↗
72025 H221Long context and tool use become the default rather than a feature. Capability gains continue; the visible constraint shifts from what models can do to liability, procurement and cost. GPT-5(2025-08-07);Claude Opus 4.5(2025-11-24) (2025-08-07) ↗
82026 H123Long-horizon agents land inside specific industry workflows. Adoption becomes sector-specific rather than general. GPT-5.5:「专为实际工作打造」 (2026-04-23) ↗
9Now24The current assessment — this point is the impact index published on the occupation's page, so the curve is anchored to a number the site already stands behind. Worth noting for the flat curves: in the same weeks, a research preview of a shared specification for AI agents to operate physical devices was opened to research labs and manufacturers. That is the first capability class pointed at the physical occupations whose lines here barely move. GPT-6 Astra(2026-09-03);Claude Fable 5.1 / Mythos 5.1(2026-09-01);Model Hardware Standard 研究预览(2026-08-27) (2026-09-03) ↗

A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.

Method and sources#

Assessment date
2026-10-01
Basis of the task judgements
5 evidence-backed · 0 platform inference · 0 not enough evidence
Verified events
10

How we assess an occupation →

← Back to Judge / magistrateThe other layer: every task, one by one →Skills, knowledge and related jobs (O*NET) →