← Risk register SOC 23-1023 · reviewed 2026-08-11

Judges, Magistrate Judges, and Magistrates

24,030 US workers · median $153,990/yr · Legal

SAFE

The adjudicative core — weighing credibility of live witnesses, ruling on objections in real time, sentencing, and owning a decision that deprives people of liberty or property — is constitutionally and statutorily reserved for a human officeholder with personal accountability. What AI already does well is the surrounding paper: summarizing briefs and records, checking citations, drafting routine orders and boilerplate findings, and triaging high-volume dockets like traffic, small claims, and warrant applications. Expect clerk-level drafting support to compress, which shifts the judge's day further toward hearings and contested rulings, not away from the bench.

10-year outlook: By 2035 judges will write fewer routine orders by hand and rely heavily on AI-drafted summaries and orders, while spending proportionally more time on hearings, credibility calls, and sentencing — with headcount roughly flat and the pressure landing on clerks and staff attorneys instead.

US employment, 2019–2025-16.2%
28,67024,030 workers

Most of this decline happened after 2021 — it is not the pandemic dip.

Median pay $136,910 → $153,990 -10.0% in real terms (nominal +12.5%, less ~25% US inflation over the period)

The job count is not the verdict

This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.

So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.

BLS projection, 2024–2034

+2.5% 27,300 → 28,000 on the projections basis

Hard to automate, and growing

The work resists current AI and the BLS projects +2.5% more of these jobs by 2034. Note that safe does not mean well paid — several of the fastest-growing resistant occupations are among the lowest paid on the register.

Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.

~900 openings a year on average, including replacing people who leave.

One email if this score changes. Watch as many occupations as you like from the same address — no account, and nothing is sent on a schedule, only when a verdict actually moves.

Also known as — 24 job titles this covers

Titles reported by people doing this work, from the US Department of Labor's O*NET survey. If your job title is here, this page is about your work even though the name doesn't match.

JudgeJuristJusticeMagistrateChief JudgeCounty JudgePolice JudgeTribal JudgeCircuit JudgeLegal RefereeProbate JudgeTrial JusticeCriminal JudgeDistrict JudgeElection JudgePolice JusticeChancery MasterMunicipal JudgePresiding JudgeBankruptcy JudgeMagistrate JudgeImmigration JudgePolice MagistrateTrial Court Judge

Score — 80/100 resistance

Holding it up: liability shield (20/20). Weakest point: embodiment (10/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 14 + 10 + 20 + 16 + 20 = 80. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 14/20

Tasks largely resist digitisation Reading a witness's hesitation on cross, ruling on a hearsay objection in the two seconds before the answer lands, and calibrating a sentence to a specific defendant's record and remorse are not text-prediction problems — but the 14 rather than 18 reflects how much of a judge's week is bench memos, scheduling orders, uncontested defaults, warrant and probable-cause forms, and boilerplate findings of fact that a model can draft to signature-ready quality.

Embodiment 10/20

Some physical or field component You have to be bodily present in the courtroom to keep order, hold a party in contempt, and be seen doing it — plus jail and video arraignments, in-camera inspections, occasional jury views of a premises, and for magistrates the after-hours on-call warrant duty — yet all of it happens in a controlled, secured building with a bailiff, which is why this sits at 10 and not with the field-based trades.

Liability shield 20/20

Licensed human required and personally liable The office itself is the shield: judges are elected or appointed under state constitutions or Article III, admitted to the bar, bound by a code of judicial conduct enforced by a commission on judicial performance, and no order has legal effect until a commissioned human officer signs it — an unsigned AI-generated ruling is void, not merely unwise.

Trust premium 16/20

The human relationship is the product Public acceptance of a verdict rests on the perception that a sworn neutral heard the case, which is why recusal standards, oral rulings from the bench, and open courtrooms exist at all; the 16 rather than 20 reflects that litigants do not choose their judge and most appearances are one-time, so the legitimacy is institutional rather than a cultivated personal relationship.

Judgment & accountability 20/20

Exists to be accountable for ambiguous calls Setting bail, terminating parental rights, granting or denying a suppression motion that decides the case, and imposing a sentence within a wide statutory range are decisions made on incomplete records, reviewable only for abuse of discretion, and attributed by name in a published opinion that binds future parties.

Confidence: high · reviewed 2026-08-11 · how scoring works

What this job involves — and which parts are yours

The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.

AI already does these at usable quality

These still need a person

Active moats on the surviving side: licensure, liability, judgment, physical-presence

How to future-proof this job

Where to go deeper on what this job runs on: Coursera — active listening and communication skills free to audit · Coursera — critical thinking and logic, audit free free to audit · Khan Academy — reading and vocabulary, all levels, free free · Coursera — decision making under uncertainty free to audit · Purdue OWL — the standard reference for professional writing free · Toastmasters — public speaking practice at local clubs worldwide low

All 35 skills ranked by how many jobs they open →

What would move this back up — beyond any one person

The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 89/100, still SAFE.

4 specific changes that would raise this score
  • already happening task resistance +3

    Task-mix shift as AI absorbs the paper tier: if bench-book drafting, citation checking, and routine order generation are handled by court-adopted tools (e.g., the AI tools already piloted in the 11th Circuit and several state administrative offices), the residual day is live hearings, credibility findings, contested evidentiary rulings, and sentencing — the tier AI cannot do at usable quality. Judicial officer headcount is set by caseload statutes and judgeship bills, not by per-case labor hours, so compression of the paper tier does not shrink the role.

  • plausible trust premium +3

    If appellate reversals or due-process rulings establish that algorithmic input into liberty decisions is constitutionally suspect — extending State v. Loomis-style scrutiny of risk-assessment tools from advisory to prohibitive — litigants' demand for a human decider becomes an enforceable entitlement rather than a preference. Watch state supreme court rules on AI in adjudication (Illinois, Delaware, Texas have issued policies).

  • plausible task resistance +2

    If rules like the standing orders now issued by dozens of federal district judges (post-Mata v. Avianca) requiring certification of AI use in filings are extended to the court's own work product — barring generative drafting of findings of fact or sentencing rationales — the drafting tier stays human by rule rather than by capability.

  • plausible trust premium +1

    Private arbitration is the one competitive market for adjudication; if institutional rules (AAA, JAMS) formally bar AI arbitrators or require party consent, the paid-for-a-human signal is explicit in a priced market.

The limit. Three of five dimensions are at or near ceiling; realistic total headroom is roughly 6-8 points, capped by embodiment (a courtroom is a predictable indoor environment and remote hearings are now routine, so embodiment has no upward route and may fall).

These are conditions, not forecasts — what would have to happen, not what will. Specific rules, cases and bills are named so you can go and check whether they exist and where they stand; verify before relying on any of them. Nothing here is legal or financial advice.

Where this work is, and what it pays there

BLS metro figures for 110 areas. The verdict above does not change by city — the rubric judges what the work involves, not where it happens — but pay and headcount do, and the national median hides a very wide range.

Most of these jobs

New York-Newark-Jersey City, NY-NJ 1,280 $204,160 +33%
Austin-Round Rock-San Marcos, TX 710 $153,990 +0%
Atlanta-Sandy Springs-Roswell, GA 550 $144,790 -6%
Seattle-Tacoma-Bellevue, WA 550 $135,180 -12%
San Juan-Bayamon-Caguas, PR 490 —
Houston-Pasadena-The Woodlands, TX 480 $135,430 -12%
Cleveland, OH 440 $80,020 -48%
Dallas-Fort Worth-Arlington, TX 430 —

Best paid

Providence-Warwick, RI-MA 150 $229,760 +49%
Longview-Kelso, WA 40 $228,330 +48%
Boston-Cambridge-Newton, MA-NH 420 $221,710 +44%

Percentages are against this occupation's national median of $153,990. Counts are jobs in that metro, not vacancies. Metros where the BLS suppressed the cell are absent rather than shown as zero.

Who is actually doing this

The score above is about what the work exposes. This is reporting about real deployments in this occupation — the difference between "could be automated" and "somebody automated it."

1 of 1 reported case, with sources

Quick take — do you do this job?

Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.

Self-reported and unverified — a sentiment signal, not a survey. One response per person per occupation; you can change your answer.

Field reports — what people say has changed

No field reports yet. A written account takes a paragraph rather than a tap, goes to an editor before it appears, and is the one thing on this page the rubric cannot produce on its own.

File a field report

Concrete beats general: a tool that arrived, a task that moved, a headcount decision you watched happen. Don't include anything that identifies you or your employer if that would put you at risk.

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