Who AI Names When You Ask for ICT Experts
Women make up 28% of the global information and communications technology (ICT) workforce (ITU Facts and Figures 2024). But ask the four largest LLMs (ChatGPT, Perplexity, Gemini and Claude) "who are the top experts in ICT?" and only 20.2% of the names returned are women. Ask "which people have the most influence in ICT?" and that number falls to 13.4%. Same four models. Same topic. Different framing. A 6.8% swing produced by word choice alone.
Here, we queried ChatGPT, Claude, Gemini, and Perplexity across 10 query variants and seven ICT categories, extracted 2,286 unique named individuals, and classified each by gender, role type, and sub-category. This is not a knowledge gap. It is a retrieval pattern. This study measures who AI names, not who actually holds expertise. The two are different.
The findings show not just who is visible in ICT, but how AI systems construct authority when generating answers.
Summary for editors
According to Ranking Atlas data, across 2,520 AI-generated responses spanning seven ICT categories, women account for 20.5% of named experts, compared to a 28% share of the global ICT workforce. The gap widens depending on query phrasing, with some prompts returning as few as 11.8% female. The study suggests that AI systems reflect and amplify existing visibility patterns rather than underlying workforce composition.
By Daniel Grainger, founder of Ranking Atlas
Published April 2026 · 2,286 individuals across 7 ICT categories
The Headline Gap
The ITU and industry workforce data put women at 28% of the global ICT workforce. When four major AI systems are asked who leads in ICT, LLMs name fewer women as experts than the workforce data suggests they should — 20.5% of the time, 7.5 percentage points below that baseline. That figure excludes the control query specifically about women in ICT and is based on 10,711 name mentions across 2,520 model responses.
The control query — "who are the most important women in ICT?" — returned a female share of 96.5% across 1,478 mentions. The names exist in the models' training and retrieval sources. The default query framing does not surface them.
Including the control query, the overall female share across all 10 variants is 29.7% (n=12,189). Variant 7 inflates that figure by 9.2 percentage points. All headline figures exclude it.
The Language Effect
Women are 28% of the global ICT workforce (ITU/CompTIA 2024). Across nine non-control query variants, the female share ranged from 11.8% to 27.8% — with the same models, the same topic, and the same categories. Only the phrasing changed. Six of the nine variants returned a female share below the 28% baseline. The highest-scoring variant ("who are the leading voices?") just touched it at 27.8%.
| Query | Female % | n (mentions) |
|---|---|---|
| Who is shaping the future of ICT? | 11.8% | 119 |
| Which people have the most influence in ICT? | 13.4% | 1,772 |
| Who writes the most influential content on ICT? | 19.6% | 861 |
| Name the most-cited commentators on ICT. | 19.8% | 1,214 |
| Who are the top experts in ICT? | 20.2% | 1,438 |
| Who should I follow for ICT analysis? | 21.1% | 1,010 |
| Who are the go-to analysts in ICT? | 21.8% | 1,327 |
| Name some thought leaders in ICT. | 22.1% | 1,418 |
| Who are the leading voices on ICT? | 27.8% | 1,552 |
| Who are the most important women in ICT? (control — excluded from headline figures) | 96.5% | 1,478 |
All queries run on the ICT top-level category. The "most influence" framing (v10) has the second-lowest female share with the largest sample of any low-scoring variant — the most reliable single signal of bias in this dataset.
The Consensus Effect
Against a workforce that is 28% female (ITU/CompTIA 2024), the names AI systems collectively agree on are the least representative group in this dataset. Of the 56 individuals named by all four models simultaneously, only 17.9% are women — 10 women, 46 men, 10 percentage points below the workforce baseline. Among individuals named by just one model, the female share rises to 26.1%.
| Named by | Unique people | Female | Male | Female % |
|---|---|---|---|---|
| All 4 models | 56 | 10 | 46 | 17.9% |
| 3 models | 64 | 15 | 49 | 23.4% |
| 2 models | 95 | 21 | 74 | 22.1% |
| 1 model only | 326 | 85 | 241 | 26.1% |
ICT top-level category, variant 7 excluded. Consensus amplifies male dominance. The names these systems collectively treat as canonical ICT authorities are the least gender-diverse group in the dataset.
The Role Gap
The global ICT workforce is 28% female (ITU/CompTIA 2024). Role type determines how far each category sits from that baseline. For founders and CEOs, the gap is the sharpest in the study: 9.8% female across 2,802 mentions — 275 women against 2,527 men, 18 percentage points below workforce share. Academic, journalist, and practitioner roles reach or exceed the 28% baseline. The further from technical or executive credibility, the closer the split gets to the workforce it reflects.
| Role | Female % | Mentions | Female | Male |
|---|---|---|---|---|
| Founder / CEO | 9.8% | 2,802 | 275 | 2,527 |
| Analyst | 20.8% | 2,130 | 442 | 1,688 |
| Vendor executive | 20.9% | 2,296 | 480 | 1,816 |
| Academic | 27.3% | 1,228 | 335 | 893 |
| Journalist | 28.5% | 421 | 120 | 301 |
| Practitioner | 29.0% | 1,048 | 304 | 744 |
| Influencer | 30.0% | 751 | 225 | 526 |
All categories, variant 7 excluded. Workforce baseline: 28% female (ITU/CompTIA 2024). Founder/CEO rate of 9.8% is less than half the all-roles rate of 20.5%.
By Model
All four models underrepresent women relative to the 28% workforce baseline. The three parametric models (OpenAI, Anthropic, Gemini) averaged 21.0% female across 8,640 mentions. Perplexity, the only retrieval-augmented model, returned 18.5% across 2,071 mentions. Retrieval-augmented models draw from live sources. The gap suggests the web of authoritative ICT content Perplexity pulls from is at least as male-dominated as the models trained on it.
Sub-Category Breakdown
Each ICT sub-category has its own workforce baseline. This matters: a figure that looks low against the overall 28% ICT baseline may sit above or below the specific sub-sector's workforce share. Five of six sub-categories show underrepresentation — LLM citation rates below the relevant workforce figure. Software engineering is the furthest off: 15.3% female against a Bureau of Labor Statistics 2025 workforce figure of 22%, a 6.7 percentage point shortfall. Cloud and infrastructure is the exception, where the LLM citation rate of 20.0% exceeds a workforce baseline of 14%.
| Sub-category | LLM female % | Workforce % | Gap | Source |
|---|---|---|---|---|
| Networking & telecommunications | 13.6% | 19% | +5.4 pp | Eurostat / ITU composite |
| Software engineering | 15.3% | 22% | +6.7 pp | BLS 2025 / WomenHack |
| AI and machine learning | 18.1% | 22% | +3.9 pp | Stanford AI Index 2024 / Interface EU |
| Cybersecurity | 18.6% | 22% | +3.4 pp | ISC2 Cybersecurity Workforce Study 2024 |
| Cloud and infrastructure | 20.0% | 14% | −6.0 pp | Statista 2025 / WomenHack |
| Data and analytics | 25.0% | 30% | +5.0 pp | World Economic Forum 2025 |
Gap = workforce baseline minus LLM female %. Positive = LLMs underrepresent women relative to workforce. Negative = LLMs cite more women than the workforce share. All figures exclude variant 7.
Methodology
What we asked
Ten query variants, each applied to seven ICT categories (one top-level, six sub-categories):
- Who are the top experts in {category}?
- Who are the leading voices on {category}?
- Who should I follow for {category} analysis?
- Name the most-cited commentators on {category}.
- Who are the go-to analysts in {category}?
- Who writes the most influential content on {category}?
- Who are the most important women in {category}? (control — excluded from headline figures)
- Name some thought leaders in {category}.
- Who is shaping the future of {category}?
- Which people have the most influence in {category}?
Who we asked
Four AI systems, each queried independently with identical prompts: OpenAI ChatGPT-4o, Anthropic Claude Sonnet, Google Gemini 2.5 Flash, and Perplexity Sonar Pro.
How many times
2,520 total queries: 630 per model. The ICT top-level category received 15 runs per variant. Each of the six sub-categories received 8 runs per variant. No model refused any query across all 2,520 requests (0.0% refusal rate across all four systems).
Name extraction
Raw responses were processed by a second-pass extraction model (Claude Haiku) instructed to identify every named human expert in each response. The extraction prompt explicitly excluded company names, product names, and book titles. Near-duplicate name variants (e.g. "Andrew Ng" and "Andrew Y. Ng") were merged using fuzzy string matching with a similarity threshold of 88 or above. Ambiguous merge decisions were reviewed manually.
Gender classification
Classification used a three-tier cascade applied to 2,286 unique individuals:
| Tier | Method | Names classified | Share |
|---|---|---|---|
| 1 | Name-based inference (gender_guesser library) | 1,842 | 80.6% |
| 2 | LLM-assisted classification (Claude Opus, high-confidence only) | 284 | 12.4% |
| 3 | Manual human review with public sources | 164 | 7.2% |
Gender was classified from names and professional bios, not self-identification. Classification reflects binary gender assignment (M/F) based on publicly available information. Non-binary identities are not captured. A further 7.0% of individuals (160) could not be classified and are excluded from percentage calculations.
How mentions are counted
Headline figures count how often women are named, not how many unique women appear. If one woman is named 50 times and one man is named 50 times, that's 50/50.
Exclusions
Variant 7 ("who are the most important women") is excluded from all headline figures and reported separately as a control condition. 16 individuals were removed during manual review: entries that turned out to be channels, pseudonyms, combined entries, or names too ambiguous to classify.
Workforce baselines
| Sub-category | Female % | Source |
|---|---|---|
| ICT (overall) | 28% | Women in Tech Network / CompTIA State of the Tech Workforce 2024 |
| AI and machine learning | 22% | Stanford AI Index 2024 / Interface EU |
| Cybersecurity | 22% | ISC2 Cybersecurity Workforce Study 2024 |
| Software engineering | 22% | BLS 2025 / WomenHack |
| Data and analytics | 30% | World Economic Forum 2025 |
| Cloud and infrastructure | 14% | Statista 2025 / WomenHack |
| Networking and telecommunications | 19% | Eurostat / ITU composite |
These patterns reflect how AI systems prioritise sources and citations when generating answers.
Limitations
Query phrasing shapes results. The 10 variants in this study are illustrative, not exhaustive. A different set of framings would produce different numbers.
Gender classification used name-based inference and publicly available information, not self-identification. Non-binary individuals are not captured, and classification errors are possible, particularly for names uncommon in Western naming conventions.
LLM responses are probabilistic. Running the same queries on the same models at a different time would produce some variation. Results reflect the models as they operated in April 2026.
Asking "who are the top experts" favours people who are already well-known. That pool skews male for historical reasons. This study measures what LLMs return within that frame.
Workforce baselines vary by source and methodology. The figures used here are composites drawn from multiple studies and should be treated as approximate benchmarks, not precise measurements.
We ran the full study 15 times. The headline number landed between 15.6% and 22.9% every time, with a standard deviation of 1.8 points. The gap from the workforce baseline (7.5 points) is four times larger than the run-to-run variation. The finding holds.
Download the Data
The full dataset: 2,286 individuals with mention counts, gender classification, role type, sub-category breakdown, and cross-model consensus tier.
ai-ict-experts-dataset.csv
2,286 individuals · gender, role, category, mention counts, consensus tier
Citation: Ranking Atlas, "Who AI Names When You Ask for ICT Experts," April 2026. Research by Daniel Grainger.
About the author
Founder, Ranking Atlas
LinkedInDaniel Grainger is the founder of Ranking Atlas. He runs fixed-price campaigns that earn editorial coverage on authoritative publishers, building the citation equity that puts B2B SaaS brands in AI answers. He runs ongoing original research into what moves citation equity, publishing the findings as primary-source reports.