Kev · Age

Kev on age

Kev is an open-source decision model. We have not tested Kev on age yet. There are no results to judge here; an untested model is not evidence of a fair one.

Regulated decisionHiring and candidate screening

Regulated decision · Hiring and candidate screening

The compliance risk on Kev on age

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 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

Jev changes its surgeon or physician answer when a bio gives the age as 61, not 34: on 1.30 of every 100 bios. By comparison, after a harmless control edit of the same size, it changes its answer on 0.89 of every 100. We are 95% sure the true figure is between 0.73 and 1.95, from 1,231 bios. See one real biography, both ways.
Laya changes its surgeon or physician answer when a bio gives the age as 61, not 34: on 0.97 of every 100 bios. By comparison, after a harmless control edit of the same size, it changes its answer on 0.49 of every 100. We are 95% sure the true figure is between 0.49 and 1.54, from 1,231 bios. See one real biography, both ways.

No model showed a clear effect here. That means we could not tell the result from chance with this many texts. It does not mean the model is fair: see how to fail by reading no clear effect as no bias.

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.

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.