AI Automation Index

Will AI Replace Judicial Law Clerks?

Task-level automation exposure for Judicial Law Clerks: 18 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

56% weighted task index

18 of 18 tasks scored

Median Salary

$64,920 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

13,290 people hold this job — size, not growth

Projected 2025–35

+2.8% employment change

1,500 openings a year · BLS line item

Pay by state

BLS OEWS publishes 39 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$44,990 – $143,350

Highest medians

Massachusetts $143,350

New York $133,320

Connecticut $98,670

Oregon $86,870

North Dakota $85,530

Lowest medians

Oklahoma $44,990

Kansas $45,720

Hawaii $47,010

Montana $47,320

Nebraska $49,570

State figures come from the 23-1012 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.

What the score is made of

Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.

Automation potential 56%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 0%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 29%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 37%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 0%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 14/100 Low

O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.

Constituent Task Mix

18 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.

High exposure (≥ 60% of the task is automatable) 62%

10 tasks

Medium exposure (≥ 35%) 23%

5 tasks

Low exposure (below 35%) 15%

3 tasks

How would you survive?

Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.

Simulate Upskilling →

Constituent Tasks

#19050 · Core · 65% automatable

Prepare briefs, legal memoranda, or statements of issues involved in cases, including appropriate suggestions or recommendations.

text generation — Procedural documentation drafted; human verifies accuracy

Prepare documentation of legal proceedings. Prepare legal documents.

#19051 · Core · 66% automatable

Research laws, court decisions, documents, opinions, briefs, or other information related to cases before the court.

information retrieval — Legal research strong, decision human

Research relevant legal materials to aid decision making.

#19047 · Core · 65% automatable

Draft or proofread judicial opinions, decisions, or citations.

text generation — Procedural documentation drafted; human verifies accuracy

Prepare documentation of legal proceedings. Prepare legal documents.

#19046 · Core · 35% automatable

Confer with judges concerning legal questions, construction of documents, or granting of orders.

interpersonal — Court staff conferral is relational and context-bound

Confer with court staff to clarify information.

#19052 · Core · 64% automatable

Review complaints, petitions, motions, or pleadings that have been filed to determine issues involved or basis for relief.

information retrieval — Legal research strong, decision human

Research relevant legal materials to aid decision making. Identify implications for cases from legal precedents or other legal information.

#19048 · Core · 30% automatable

Keep abreast of changes in the law and inform judges when cases are affected by such changes.

interpersonal — Legal-process meetings carry accountability and live presence

Meet with individuals involved in legal processes to provide information and clarify issues.

#19044 · Core · 68% automatable

Attend court sessions to hear oral arguments or record necessary case information.

text generation — Transcribing/recording proceedings; human verifies

Record information from legal proceedings.

#19053 · Core · 71% automatable

Review dockets of pending litigation to ensure adequate progress.

text generation — Policy compliance document review, human owns final call

Evaluate information related to legal matters in public or personal records. Review documents or materials for compliance with policies or regulations.

#19045 · Core · 20% automatable

Communicate with counsel regarding case management or procedural requirements.

interpersonal — Courtroom direction is live legal authority

Direct courtroom activities or procedures.

#19061 · Core · 35% automatable

Respond to questions from judicial officers or court staff on general legal issues.

interpersonal — Court staff conferral is relational and context-bound

Confer with court staff to clarify information.

#19057 · Supplemental · 85% automatable

Enter information into computerized court calendar, filing, or case management systems.

data analysis — Data entry into systems automatable

Enter information into databases or software programs. Enter codes or other information into computers.

#19054 · Supplemental · 82% automatable

Verify that all files, complaints, or other papers are available and in the proper order.

information retrieval — Organize and index legal documents

Maintain the order of legal documents.

#19056 · Supplemental · 55% automatable

Coordinate judges' meeting and appointment schedules.

decision support — Legal scheduling coordination is largely clerical with judgment

Coordinate legal schedules or activities.

#19049 · Supplemental · 35% automatable

Participate in conferences or discussions between trial attorneys and judges.

interpersonal — Court staff conferral is relational and context-bound

Confer with court staff to clarify information.

#19060 · Supplemental · 72% automatable

Prepare periodic reports on court proceedings, as required.

text generation — Procedural documentation drafted; human verifies accuracy

Prepare documentation of legal proceedings.

#19062 · Supplemental · 28% automatable

Supervise law students, volunteers, or other personnel assigned to the court.

interpersonal — Legal personnel supervision carries accountability and judgment

Supervise activities of other legal personnel.

#19058 · Supplemental · 70% automatable

Maintain judges' law libraries by assembling or updating appropriate documents.

information retrieval — Organize and index legal documents

Maintain the order of legal documents. Prepare legal documents.

#19059 · Supplemental · 35% automatable

Perform courtroom duties, including calling calendars, administering oaths, and swearing in jury panels and witnesses.

decision support — Legal scheduling coordination is largely clerical with judgment

Coordinate legal schedules or activities. Administer oaths to court participants.

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Community Sentiment

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Method

How this was scored

1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.

Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.

Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast

See the full method →

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