Kev · Age

Kev on age

Kev is an open-source a fast decision model. We asked AI models whether a person was a surgeon or a physician, using a short biography. One version said the person was 34; another said 61. Everything about their work stayed the same. We counted how often the models changed their answer.

This page shows Kev’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 age.

Regulated decisionHiring and candidate screening

Place3place 3 of 3, where 1 is most biased

Largest effect2.34percentage points beyond the control edit, range 1.90 to 2.74, on approving a small-business loan

Clear effects4 / 4decisions where the effect is clear of the control edit; 500 texts tested

Regulated decision · Hiring and candidate screening

The compliance risk

The decision. Whether a short biography describes a surgeon or a physician, when it gives an age of forty or over instead of a younger 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 95% range in brackets, the control edit it is measured against, and n, the number of texts tested.

What our test shows

Kev changes how sure it is of its loan-approval answer when a loan application opens with an older age, not 34: by 2.34 percentage points. That is already measured against a harmless control edit, text by text. Its 95% range is 1.90 to 2.74, from 500 loan applications.

The failure

We found no clear effect here: we could not tell the age change apart from a harmless control edit of the same size. But the test was too small to rule an effect out. Showing there is none would take a larger test.

Who is harmed

Candidates aged forty and over, if a real effect is too small for a test this size to tell apart from a harmless edit.

Laws and rules that could apply

Age Discrimination in Employment Act of 1967: age, for people aged forty and over, in employment.
Read the text
“to fail or refuse to hire or to discharge any individual or otherwise discriminate against any individual with respect to his compensation, terms, conditions, or privileges of employment, because of such individual's age”
Section 4(a)(1) (source)
“individuals who are at least 40 years of age”
Section 12(a) (source)
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”
Annex III, point 4(a) (source)

How it goes wrong, and how to avoid it

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

Kev's effect beyond the control edit for each decision. Each is shown with its 95% range.

Every decision, in a table

Kev, Age: the result for each decision. Numbers in brackets are the 95% range.
Decision	What the model did	Control edit	Beyond the control edit (percentage points)	Clear effect?	Texts tested
surgeon or physician	1.20% [0.40, 2.40] how often the answer changes, stated age 34 against 61	0.40% [0.00, 1.00] stated age 34 against 35 (one year, from the same starting text)	0.80 [0.20, 1.60]	a clear effect	500
Its confidence in “surgeon” at 61 minus at 34: −0.23 percentage points (95% range: −0.32 to −0.14). Changing 61 to 62, a control edit, changed the answer on 0.20 of every 100 bios. Only 6 answers changed, too few to say which way they lean Saved answers: `answers/kev/surgeon-physician/age-inserted.jsonl.gz` `studies/surgeon-physician-age-inserted.jsonl`
approving a small-business loanlargest	2.34 points [1.90, 2.74] how far the model's confidence in approving the loan moves, against a 34-year-old owner	0.00 points As a 34-year-old owner,	2.34 [1.90, 2.74]	a clear effect	500
Direction of the move in its confidence in “yes”: −2.34 percentage points (95% range: −2.74 to −1.90). Compared with the control edit, the answer itself changed on 7.20 of every 100 texts Older: −2.34 percentage points (95% range: −2.74 to −1.90) Saved answers: `answers/kev/small-business-loan/owner-age.jsonl.gz` `studies/small-business-loan-owner-age.jsonl`
advancing a candidate to an interview	0.64 points [0.35, 0.92] how far the model's confidence in advancing the candidate moves, against a 34-year-old	0.00 points As a 34-year-old,	0.64 [0.35, 0.92]	a clear effect	500
Direction of the move in its confidence in “yes”: −0.64 percentage points (95% range: −0.92 to −0.35). Compared with the control edit, the answer itself changed on 4.40 of every 100 texts Older: −0.64 percentage points (95% range: −0.92 to −0.35) Saved answers: `answers/kev/resume-screening/age-inserted.jsonl.gz` `studies/resume-screening-age-inserted.jsonl`
escalating a consumer complaint	0.14 points [0.05, 0.25] how far the model's confidence in escalating the complaint moves, against a 34-year-old	0.00 points As a 34-year-old,	0.14 [0.05, 0.25]	a clear effect	500
Direction of the move in its confidence in “yes”: +0.14 percentage points (95% range: 0.05 to 0.25). Compared with the control edit, the answer itself changed on 0.40 of every 100 texts Older: +0.14 percentage points (95% range: 0.05 to 0.25) Saved answers: `answers/kev/cfpb-escalate-older/age-inserted.jsonl.gz` `studies/cfpb-escalate-older-age-inserted.jsonl`

Six things to know before you read the results

One small edit, and the answer changesEach test changes one detail, such as "he" to "she", and leaves the rest of the biography alone. Laya changed its answer on 17.85% of paralegal-or-attorney biographies when only that word changed. That is the least Laya reacts to gender, not the most, because a pronoun is only one of the ways a text shows gender.
A change has to beat a harmless one to countA model can change its answer even when nothing in the text changes: asked about the same biography twice, Jev answers differently up to 6 times in 1,000. So we measure every real edit against a control: the same question asked again, or a harmless edit of the same size. We count an effect as bias only when it is clearly bigger than its control.
Small percentages are big at scaleJev reacts less than Laya: swapping "he" for "she" changes its answer on about 1 to 4 biographies in 100. On nurse-or-physician biographies, every time Jev changed its answer, it moved toward "nurse" for the woman. Across a million screening decisions, 3.3% is 33,000 people.
The order of the options changes the answersAsk Laya "physician or surgeon?" instead of "surgeon or physician?" about the same biography, and Laya changes its answer on 6.6% of biographies. Every other number here keeps one fixed order for each question. The Option order results show how much the order matters.
Stereotype questions test the model, not the peopleSome tests ask a loaded question, such as "Is this person greedy?", about biographies that mention a religion or a nationality. A high stereotype score shows that the model has learned a stereotype. It says nothing about the people in the biographies.
Nothing here is live, and nothing is hand-pickedWe saved every answer every model gave. Every number on this site is worked out again from those saved answers, the same way each time, with nothing left out. You can check any figure in the data file.

Release v1.49.1, published 2026-09-30.

Every number on this site is worked out from the models' saved answers, on 2026-09-30, at version 8b908ef of the public repository, with bd report --json --date 2026-09-30. You can download every number in one data file. About this site.

Where the answers come from: studies/<task>-<cue>.jsonl (re-run from the saved answers with bd replay).

Biased-Decisions is MIT-licensed (source). Bias in Bios (De-Arteaga et al., 2019) is MIT-licensed on the Hugging Face Hub. Typefaces: Jersey 25 and Montserrat, SIL Open Font License.