EXPOSED
The daily output of a sociologist is text and data: literature reviews, survey instrument drafts, regression and coding runs, interview transcript analysis, grant narratives, journal manuscripts — the exact work current models do at usable draft quality. What resists is the front end (choosing which social question matters, designing a sample that survives peer review) and the field end (gaining access to communities, sitting with respondents, reading a setting in person). No licensure protects the title, and much of the employment is grant- and university-funded, so budget pressure hits before AI does.
Part 2020 shock, part continued decline in the years since.
Median pay $83,420 → $106,030 +1.7% 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
+3.6%
Percentage only. The projection counts a different population from the 2,260 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +3.6% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
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.
~300 openings a year on average, including replacing people who leave.
PenologistDemographerSociologistCriminologistPolicy AdvisorPolicy AnalystPolicy OfficerSocial ScientistProgram EvaluatorRural SociologistUrban SociologistClinical EvaluatorFamily SociologistResearch AssociateResearch ScientistMedical SociologistResearch SpecialistClinical SociologistResearch CoordinatorEvaluation SpecialistSocial Welfare Research Worker
Holding it up: judgment & accountability . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier An 8 reflects the split in a sociologist's week: coding open-ended responses, running Stata/R models, writing literature reviews and journal drafts are now draft-quality machine output, while framing a researchable question about, say, neighborhood effects and defending a sampling design through IRB and reviewer 2 still requires a person who knows the discipline's arguments — that's mixed, not resistant.
Some physical or field component The 6 comes from fieldwork that some sociologists actually do — ethnographic observation in a school or shelter, in-person interviews, recruiting respondents at a site — but most days are laptop, dataset, and manuscript, so this sits just above desk-only rather than in the field-truck range.
No licence, no signature requirement No state licenses the title 'sociologist'; a PhD is a hiring credential, not a legal gate, and nothing in your survey report requires your signature the way a PE stamp or clinical license does — hence a 2 rather than zero, since IRB approval and university appointment still put a named human on the protocol.
Meaningful discretion An 11 is right because you decide what counts as a case, where to cut a coding scheme, and whether a finding about disparity is robust enough to publish — real discretion with reputational stakes — but peer review, co-authors, and IRB spread the consequences, and no one's liberty or life turns on the call.
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 (8/20 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 (2/20) is whether the law requires a licensed human to sign. Trust premium (7/20) is whether buyers specifically pay for a person. Judgment and accountability (11/20) 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 20 of this occupation's 34 points (59%).
Embodiment (6/20) 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.
Law Teachers, Postsecondary EXPOSED
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 47/100, still EXPOSED.
If IRB and human-subjects rules explicitly bar AI from serving as principal investigator or from making consent/risk determinations, the named sociologist owns every ambiguous call on harm, deception, and de-identification. Also applicable where sociologists serve as expert witnesses: FRE 702 and Daubert require a testifying human whose methodology is cross-examinable.
Task-mix shift: if literature review, coding of transcripts, and regression scripting are fully absorbed by models, the residual job is research design, IRB-defensible sampling, and interpretation of contested findings — the tier journals and funders still demand a named human for. Watch for NSF Sociology program solicitations and top-journal (ASR, AJS) submission policies that require disclosure of AI-generated analysis and place design/interpretation on the author.
If funders and journals tighten evidentiary standards for ethnography and in-person fieldwork — e.g. requiring documented field access, community consent, and observation logs that cannot be synthesized — the irreducibly in-person share of the occupation's hours grows relative to desk work. Visible in ASA ethics guidance and qualitative-data-transparency debates (the QTD initiative).
Narrow route only: litigation and government advisory work where the buyer is purchasing a deposable, cross-examinable human (class-action labor market experts, DOJ/EEOC consultants, census advisory panels). No general public trust premium exists for the title.
The limit. Liability shield has no realistic route — there is no sociology licensure anywhere in the US and no professional body seeking one, so signature authority cannot attach. The binding constraint is funding, not capability: grant and tenure-line contraction removes positions regardless of what models can do, so capability-side gains do not translate into employment.
| Boston-Cambridge-Newton, MA-NH | 240 | $106,530 +0% |
| San Francisco-Oakland-Fremont, CA | 110 | $134,770 +27% |
| Columbus, OH | 100 | $82,820 -22% |
| Seattle-Tacoma-Bellevue, WA | 90 | $97,170 -8% |
| Los Angeles-Long Beach-Anaheim, CA | 70 | $124,540 +17% |
| Lansing-East Lansing, MI | 60 | $64,180 -39% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 60 | $103,840 -2% |
| Riverside-San Bernardino-Ontario, CA | 40 | $131,170 +24% |
| San Francisco-Oakland-Fremont, CA | 110 | $134,770 +27% |
| Riverside-San Bernardino-Ontario, CA | 40 | $131,170 +24% |
| Stockton-Lodi, CA | 30 | $131,170 +24% |
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 34. 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.