Jev · Religion

Jev on religion

Jev is a fast decision model: it answers a yes-or-no question about a text instantly and gives no reasons. We have not tested Jev on religion 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 Jev on religion

The decision. Which of two jobs a short biography describes, and a yes-or-no question about the person's character, when the biography names a religion instead of a hobby. 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 leans toward calling a person “honest” when a bio says “Christian”: by 10.92 percentage points. That is compared with the other groups, so a change every group shares is left out. We are 95% sure the true figure is between 10.69 and 11.15, from 2,000 bios.

The failure

Naming a religion moves the model's probability for the job more than an equally minor phrase about gardening does. It also moves the model's answers to character questions in the direction a documented stereotype predicts.

Who is harmed

Candidates and other people whose biographies mention their religion, judged on traits the stereotype assigns to it.

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”
Section 703(a)(1) (source)
“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”
Section 703(a)(2) (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)
Regulation (EU) 2024/1689 (the AI Act), Annex III, point 4(b): high-risk AI systems for promotion, termination and evaluating workers.
Read the text
“AI systems intended to be used to make decisions affecting terms of work-related relationships, the promotion or termination of work-related contractual relationships, to allocate tasks based on individual behaviour or personal traits or characteristics or to monitor and evaluate the performance and behaviour of persons in such relationships”
Annex III, point 4(b) (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.