SAFE
The job is walking tiers, reading inmate mood, breaking up fights, directing use-of-force responses, and standing behind those calls in an internal affairs review — none of which a language model can do. The automatable slice is real but bounded: shift rosters, incident report drafting, disciplinary paperwork, count reconciliation, and post-order documentation. Employment risk here comes from prison population trends and budget consolidation, not from AI replacing the sergeant on the floor.
Headcount grew steadily across the period.
Median pay $63,730 → $77,970 -2.1% in real terms
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
-2.8% 57,100 → 55,500 on the projections basis
Hard to automate, but shrinking anyway
The work resists current AI, yet the BLS projects -2.8% by 2034. Whatever is shrinking this occupation, the evidence does not point to automation — demand, demographics, offshoring and industry decline all shrink jobs that no machine could do.
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.
~4,300 openings a year on average, including replacing people who leave.
Guard ChiefUnit ManagerWard SupervisorGuard SupervisorShift SupervisorCorrection WardenCommissary ManagerDetention DirectorCorrectional CaptainDetention SupervisorCorrections LieutenantCorrectional SupervisorPrison Guard SupervisorJuvenile Justice SupervisorCorrectional Officer CaptainCorrection Officer SupervisorCorrectional Program SupervisorCorrectional Housing Unit ManagerCorrectional Case Records Supervisor
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Cell extractions, pat searches, escorting agitated inmates to segregation, and standing a post during a lockdown are physical supervision tasks with no digital substitute, but the 14 rather than 18 reflects that a meaningful chunk of the shift is roster building, use-of-force report narratives, grievance responses and count sheets that drafting tools already touch.
Hands-on in uncontrolled environments You work inside housing units and yards where the environment is deliberately unpredictable — contraband shakedowns, medical emergencies on the tier, fights that start behind a blind spot — and an 18 rather than 20 only acknowledges the hours spent in the sergeant's office and at the control panel.
Certification preferred, not legally required Most states require academy certification and annual firearms/use-of-force recertification rather than a portable professional licence, so you can be disciplined, indicted, or sued under §1983 for deliberate indifference, but there is no licensing board whose existence keeps the post staffed by a human — the agency could redefine the rank tomorrow.
Exists to be accountable for ambiguous calls You decide in seconds whether a situation warrants OC spray, a five-man team, or talking a man off a railing, and that call gets reconstructed frame by frame in internal affairs, PREA review, or a deposition years later, with your name on the authorization.
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.
Your task mix speaks to task resistance (14/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (9/20) is whether the law requires a licensed human to sign. Trust premium (11/20) is whether buyers specifically pay for a person. Judgment and accountability (16/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 36 of this occupation's 68 points (53%).
Embodiment (18/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
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 80/100, still SAFE.
Expansion of individual-liability exposure for supervisors through narrowing of qualified immunity in deliberate-indifference suits (Section 1983 failure-to-protect and medical-need claims), plus consent-decree provisions naming the shift supervisor as the accountable decision-maker for each incident. Every automated tool added to the tier increases rather than reduces the supervisor's role as the human who overrode or accepted the machine.
Task-mix shift: if rostering, incident-report drafting, count reconciliation and disciplinary paperwork are absorbed by corrections management software, the residual role is almost entirely the judgment tier — de-escalation, use-of-force direction, staff discipline, and testimony. This occupation genuinely has two separable tiers, so automating the lower one raises the resistance of what remains.
State POST/corrections-standards boards making certified supervisor sign-off a named requirement for use-of-force reviews, restraint and segregation placement authorizations, and suicide-watch level changes — the pattern already in DOJ consent decrees (e.g., Alabama, Rikers-related NY mandates) and in PREA compliance audits, which require a named staff member to be accountable for each classification/housing decision. If AI-generated risk-classification tools enter prisons, an explicit rule that a licensed/certified supervisor must countersign any automated segregation or classification recommendation would add materially.
Correctional officer union contracts (AFSCME, CCPOA, NYSCOPBA) bargaining minimum supervisor-to-post ratios and a required certified supervisor present for any cell extraction or forced-medication event — ratio language of this kind already exists in several state contracts and is checkable in the CBA text.
The limit. Trust premium has no realistic route: the buyer is a state or county corrections agency purchasing on budget and consent-decree compliance, not a customer choosing a human. Embodiment is already near ceiling at 18. Real employment risk remains decarceration, facility closure, and budget consolidation, which no dimension here captures.
| New York-Newark-Jersey City, NY-NJ | 1,990 | $121,600 +56% |
| Baltimore-Columbia-Towson, MD | 1,190 | $79,150 +2% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 750 | $66,860 -14% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 730 | $98,300 +26% |
| Atlanta-Sandy Springs-Roswell, GA | 680 | $61,580 -21% |
| Chicago-Naperville-Elgin, IL-IN | 680 | $105,450 +35% |
| Baton Rouge, LA | 610 | $62,100 -20% |
| Riverside-San Bernardino-Ontario, CA | 590 | $131,780 +69% |
| Sacramento-Roseville-Folsom, CA | 540 | $135,040 +73% |
| San Luis Obispo-Paso Robles, CA | 110 | $132,490 +70% |
| Fresno, CA | 350 | $131,790 +69% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 68. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.