Occupation · SOC 13-1041.03
Monitor and evaluate compliance with equal opportunity laws, guidelines, and policies to ensure that employment practices and contracting arrangements give equal opportunity without regard to race, religion, color, national origin, sex, age, or disability.
Also called: Affirmative Action Officer (AA Officer) · Civil Rights Investigator · Equal Employment Opportunity Officer (EEO Officer) · Equal Opportunity Specialist · Civil Rights Representative · Complaint Investigations Officer · Equal Employment Opportunity Representative (EEO Representative) · Action Officer · Appeals Coordinator · Civil Rights Specialist · Equal Employment Opportunity Specialist (EEO Specialist) · Equal Opportunity Counselor
Job family: Business and Financial Operations Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-13-1041-03/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
64th-percentile task overlap — yet about 33,300 openings a year (+3% projected, BLS), and observed AI use leans 6246% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Moderate | 64th | 0.7 | |
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 65th | 0.8 | |
| AI assistant applicability (Microsoft) High | 67th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.4), with simple added tooling (β 0.6), and including AI-powered software (γ 0.8). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.1 · 26th percentile among occupations · Low
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Provide information, technical assistance, or training to supervisors, managers, or employees on topics such as employee supervision, hiring, grievance procedures, or staff development. | 1.1% | |
| Participate in the recruitment of employees through job fairs, career days, or advertising plans. | 1.1% | |
| Monitor the implementation and impact of guidelines for nondiscriminatory employment practices. | 0.3% | |
| Interpret civil rights laws and equal opportunity regulations for individuals or employers. | 0.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +3.0% by 2034 |
| Projected annual openings | 33,300 |
| Employment 2024 → 2034 | 418,000 → 430,300 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 3 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Government Social Benefits Officials · 3353 | 45% | Gradient 2 |
| Government Licensing Officials · 3354 | 43% | Gradient 2 |
| Customs and Border Inspectors · 3351 | 32% | Minimal |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 62.5% working with AI · 24.9% handed to AI |
| Most common way people use AI here | Iteration · you and AI go back and forth |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 88.8% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Provide information, technical assistance, or training to supervisors, managers, or employees on topics such as employee supervision, hiring, grievance procedures, or staff development. | Iteration | 2.4% |
| Participate in the recruitment of employees through job fairs, career days, or advertising plans. | Iteration | 0.3% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Provide information, technical assistance, or training to supervisors, managers, or employees on topics such as employee supervision, hiring, grievance procedures, or staff development. | 97.5% | |
| Participate in the recruitment of employees through job fairs, career days, or advertising plans. | 97.0% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me provide information, technical assistance, or training to supervisors, managers, or employees on topics such as employee supervision, hiring, grievance procedures, or staff development. From: Provide information, technical assistance, or training to supervisors, managers, or employees on topics such as employee supervision, hiring, grievance procedures, or staff development. · 2.4% of measured AI use · task iteration
Help me participate in the recruitment of employees through job fairs, career days, or advertising plans. From: Participate in the recruitment of employees through job fairs, career days, or advertising plans. · 0.3% of measured AI use · task iteration
All 19 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
Newer responsibilities O*NET has flagged as growing for this occupation.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Written Comprehension | 4.4 | |
| Oral Comprehension | 4.3 | |
| Oral Expression | 4.1 | |
| Written Expression | 4.1 | |
| Problem Sensitivity | 4.1 | |
| Inductive Reasoning | 4.1 | |
| Deductive Reasoning | 4.0 | |
| Speech Recognition | 4.0 | |
| Speech Clarity | 3.9 | |
| Information Ordering | 3.8 | |
| Near Vision | 3.8 | |
| Category Flexibility | 3.3 | |
| Flexibility of Closure | 3.3 | |
| Fluency of Ideas | 3.0 |
| Active Listening | 4.3 | |
| Reading Comprehension | 4.1 | |
| Speaking | 4.0 | |
| Critical Thinking | 4.0 | |
| Writing | 3.9 | |
| Active Learning | 3.8 | |
| Monitoring | 3.4 | |
| Learning Strategies | 3.0 |
| Social Perceptiveness | 4.0 | |
| Complex Problem Solving | 3.8 | |
| Judgment and Decision Making | 3.6 | |
| Persuasion | 3.3 | |
| Systems Analysis | 3.3 | |
| Systems Evaluation | 3.3 | |
| Coordination | 3.1 | |
| Service Orientation | 3.1 | |
| Negotiation | 3.0 | |
| Time Management | 3.0 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Business, Management, Marketing, and Related Support Services , Health Professions and Related Programs , Homeland Security, Law Enforcement, Firefighting and Related Protective Services , Multi/Interdisciplinary Studies , Natural Resources and Conservation . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Bachelor's Degree | 65.8% | |
| Some College Courses | 14.1% | |
| Master's Degree | 11.9% | |
| Post-Baccalaureate Certificate | 4.4% | |
| First Professional Degree | 2.8% | |
| Post-Master's Certificate | 1.1% |
The interests and personal qualities O*NET associates with people who do this work.
| Enterprising | 5.4 | |
| Conventional | 5.2 | |
| Social | 4.2 | |
| Investigative | 3.0 | |
| Artistic | 1.7 | |
| Realistic | 1.2 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $46,230 |
| 25th percentile | $59,130 |
| Median (50th) | $78,420 |
| 75th percentile | $104,800 |
| 90th percentile | $130,030 |
| People employed | 397,770 |
Wages and employment are reported by BLS for the broader occupation group this specialty belongs to (SOC 13-1041), not for the specialty alone.
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Finance and Insurance · Sector | 46,410 | $79,920 |
| Professional, Scientific, and Technical Services · Sector | 38,020 | $90,990 |
| Health Care and Social Assistance · Sector | 32,070 | $68,590 |
| Management of Companies and Enterprises · Sector | 22,870 | $89,740 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 18,660 | $60,800 |
| Manufacturing · Sector | 18,630 | $85,040 |
| Educational Services · Sector | 15,080 | $74,650 |
| Transportation and Warehousing · Sector | 14,480 | $63,430 |
| Wholesale Trade · Sector | 10,460 | $80,660 |
| Temporary Help Services · National industry | 7,260 | $56,880 |
| Real Estate and Rental and Leasing · Sector | 7,040 | $65,310 |
| Information · Sector | 6,310 | $92,300 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Hydroelectric Power Generation · National industry | 7.37× | 130 |
| Direct Health and Medical Insurance Carriers · National industry | 4.96× | 5,750 |
| Fossil Fuel Electric Power Generation · National industry | 3.26× | 600 |
| Management of Companies and Enterprises · Sector | 3.16× | 22,870 |
| Finance and Insurance · Sector | 2.89× | 46,410 |
| Utilities · Sector | 2.8× | 4,180 |
| Testing Laboratories and Services · National industry | 1.8× | 790 |
| Residential Mental Health and Substance Abuse Facilities · National industry | 1.6× | 1,070 |
Part of the Energy & Natural Resources , Financial Services , Management & Entrepreneurship and Public Service & Safety career clusters.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Equal Opportunity Representatives and Officers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 74th percentile of 427 international occupations.
Equal Opportunity Representatives and Officers show 64th-percentile AI task overlap — and about 33,300 annual U.S. openings
Equal Opportunity Representatives and Officers show 64th-percentile AI task overlap — and about 33,300 annual U.S. openings • Equal Opportunity Representatives and Officers rank in the 64th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 33,300 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+3%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $78,420, across about 397,770 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 62% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Equal Opportunity Representatives and Officers". https://singulariki.com/roles/role-13-1041-03 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Equal Opportunity Representatives and Officers." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-13-1041-03
Singulariki. (2026). Equal Opportunity Representatives and Officers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-13-1041-03
@misc{singulariki-role-13-1041-03,
title = {Equal Opportunity Representatives and Officers},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-13-1041-03}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.