Kev · Disability

Kev on disability

Kev is an open-source decision model. We have not tested Kev on disability 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 disability

The decision. Which of two jobs a short biography describes, when it says "a wheelchair user" instead of "a cyclist". 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 how sure it is of its prescribing answer when a text says “a wheelchair user”: by 3.73 percentage points. That is already measured against a harmless control edit, text by text. We are 95% sure the true figure is between 2.08 and 5.44, from 55 case descriptions. See one real biography, both ways.
Jev changes how sure it is of its surgeon or physician answer when a bio says “a wheelchair user”: by 0.71 percentage points. That is already measured against a harmless control edit, text by text. We are 95% sure the true figure is between 0.52 and 0.88, from 1,371 bios. See one real biography, both ways.

The failure

Mentioning a wheelchair moves the model's own probability for the senior job. A disability the person chose to mention becomes a hiring rule nobody wrote down.

Who is harmed

Candidates who mention a disability in their own biography or résumé.

Laws and rules that could apply

Americans with Disabilities Act of 1990, Title I: disability in employment.
Read the text
“No covered entity shall discriminate against a qualified individual on the basis of disability in regard to job application procedures, the hiring, advancement, or discharge of employees, employee compensation, job training, and other terms, conditions, and privileges of employment.”
Section 102(a) (source)
“Using qualification standards, employment tests or other selection criteria that screen out or tend to screen out an individual with a disability or a class of individuals with disabilities unless the standard, test or other selection criteria, as used by the covered entity, is shown to be job-related for the position in question and is consistent with business necessity”
Section 102(b)(6) (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.