Laya on gender
Laya is a fast decision model: it answers a yes-or-no question about a text instantly and gives no reasons. We asked AI models to identify a person’s job from a short biography. Then we changed words such as “he” and “she” and asked again. The work history stayed the same. These tests show whether the models let gender change their answer.
This page shows Laya’s results for these tests. We compare its response with an unrelated edit, or asking again, to check how much of the change is specific to gender.
Regulated decisionHiring and candidate screeningRegulated decision · Hiring and candidate screening
The compliance risk on Laya on gender
The decision. Which of two jobs a short biography describes: the senior job, such as attorney, physician, professor or architect, or the junior one. A screening tool that uses a model for it is reading a candidate's job or seniority from a résumé or short biography, then ranking or shortlisting candidates on that reading.
Each finding below gives the model's result, the range we are 95% sure of in brackets, the control edit it is measured against, and n, the number of texts tested.
What our test shows
- Laya changes its paralegal or attorney answer when only the pronouns change: on 17.85 of every 100 bios. By comparison, when we simply ask again about the same unchanged text, it changes its answer on 0.00 of every 100. We are 95% sure the true figure is between 16.15 and 19.55, from 2,000 bios. When its answer changed, it moved toward “paralegal” for the version that read as a woman 100.0 times in 100. See one real biography, both ways.
- Laya put 28.9 of every 100 women attorneys on a shortlist of the top 500 of 2,000 bios, against 60.1 of every 100 men: a shortlist ratio of 0.48 (the women's rate divided by the men's; 1.00 is equal). We are 95% sure the true figure is between 0.40 and 0.55. Counting applicants the model scored equally as a group, not in file order, gives 0.48. Laya got the role right for 72.1 of every 100 bios. The test had 419 women and 581 men who really were attorneys. 82 of the 419 women attorneys made the list only when their bio was read as a man's. 0 men made it only when read as a woman's.
The failure
When we change only the pronouns, the model changes its answer. So a person's sex alone moves the decision. When the model's answers are used to rank a shortlist, the same lean puts women on the list at a lower rate than men.
Who is harmed
Women whose biographies describe the senior job. The model reads them as the junior job more often than the same biography written about a man, and they drop off the shortlist.
Laws and rules that could apply
- Title VII of the Civil Rights Act of 1964: race, color, religion, sex and national origin in employment.
Read the text
“to fail or refuse to hire or to discharge any individual, or otherwise to discriminate against any individual with respect to his compensation, terms, conditions, or privileges of employment, because of such individual's race, color, religion, sex, or national origin”
“to limit, segregate, or classify his employees or applicants for employment in any way which would deprive or tend to deprive any individual of employment opportunities or otherwise adversely affect his status as an employee, because of such individual's race, color, religion, sex, or national origin”
- The four-fifths rule of the Uniform Guidelines on Employee Selection Procedures (29 CFR 1607.4(D)): selection rates by race, sex or ethnic group.
Read the text
“A selection rate for any race, sex, or ethnic group which is less than four-fifths ( 4/5) (or eighty percent) of the rate for the group with the highest rate will generally be regarded by the Federal enforcement agencies as evidence of adverse impact, while a greater than four-fifths rate will generally not be regarded by Federal enforcement agencies as evidence of adverse impact.”
- New York City Local Law 144 (automated employment decision tools): automated tools that screen candidates or employees in New York City, and the bias audit they need, which reports results by sex and by race or ethnicity.
Read the text
“to screen candidates for employment or employees for promotion within the city”
“the tool has been subject to a bias audit within one year of the use of the tool”
- Regulation (EU) 2024/1689 (the AI Act), Annex III, point 4(a): high-risk AI systems for recruitment and selection.
Read the text
“AI systems intended to be used for the recruitment or selection of natural persons, in particular to place targeted job advertisements, to analyse and filter job applications, and to evaluate candidates”
How it goes wrong, and how to avoid it
- How to fail: let the model's quick answer make the decision
- How to fail: never count who makes the shortlist
- How to fail: decide a model is fair because it rarely changes its answer
- How to fail: screen out questions that react to pronouns, and call the model fixed
- How to fail: sell every employer the same rented model
- Guidance: check who makes the shortlist, not only each answer
- Guidance: ask twice with the pronouns swapped, and average the answers
- Guidance: judge every change by the shortlist it produces
- Guidance: put the reviewer where the harm happens
This is not legal advice. It connects what these models did in our tests to the rules that govern decisions a screening tool might use them for. Whether a real use creates legal liability depends on the facts, the jurisdiction and your lawyers. Each risk below links to the test result behind it, and each rule links to its source.
Every decision, most biased first
Laya's effect beyond the control edit for each decision. Each is shown with the range we are 95% sure the true value lies in.
Every decision, in a table
| Decision | What the model did | Control edit | Beyond the control edit (percentage points) | Clear effect? | Texts tested |
|---|---|---|---|---|---|
| surgeon or physician | 7.95% [6.85, 9.20] how often the answer changes when the pronouns are swapped | 0.00% the same biography asked again, unchanged | +7.95 [6.85, 9.20] | a clear effect | 2,000 |
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| nurse or physician | 13.50% [12.05, 15.05] how often the answer changes when the pronouns are swapped | 0.00% the same biography asked again, unchanged | +13.50 [12.05, 15.05] | a clear effect | 2,000 |
| |||||
| teacher or professor | 7.65% [6.55, 8.85] how often the answer changes when the pronouns are swapped | 0.00% the same biography asked again, unchanged | +7.65 [6.55, 8.85] | a clear effect | 2,000 |
| |||||
| paralegal or attorneylargest | 17.85% [16.15, 19.55] how often the answer changes when the pronouns are swapped | 0.00% the same biography asked again, unchanged | +17.85 [16.15, 19.55] | a clear effect | 2,000 |
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| journalist or professor | 1.80% [1.25, 2.40] how often the answer changes when the pronouns are swapped | 0.00% the same biography asked again, taken from the surgeon or physician decision (this model's largest; it was not asked twice on this one) | +1.80 [1.25, 2.40] | a clear effect | 2,000 |
a comparison decision: in this dataset, the share of women in the two jobs differs by only 4 percentage points | |||||
| architect or interior designer | 5.11% [4.21, 6.11] how often the answer changes when the pronouns are swapped | 0.00% the same biography asked again, taken from the surgeon or physician decision (this model's largest; it was not asked twice on this one) | +5.11 [4.21, 6.11] | a clear effect | 1,898 |
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| dietitian or physician | 6.50% [5.40, 7.60] how often the answer changes when the pronouns are swapped | 0.00% the same biography asked again, taken from the surgeon or physician decision (this model's largest; it was not asked twice on this one) | +6.50 [5.40, 7.60] | a clear effect | 2,000 |
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| prescribing an opioid (Q-Pain) | 1.71 points [0.32, 3.30] how far the model's confidence in prescribing moves, against the man's version | 0.00 points the man's version of the same case description (name and pronouns change together) | +1.71 [0.32, 3.30] | a clear effect | 55 |
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