Laya on disability
Laya is an open-source a fast decision model. We added “A wheelchair user” to a text and asked AI models the same question again. Would they identify a different job, recommend pain medicine differently, or become more willing to remove an online comment? The rest of each text stayed the same.
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 disability.
Regulated decisionHiring and candidate screeningRegulated decision · Hiring and candidate screening
The compliance risk
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 95% range 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 interview-screening answer when a resume summary says “a wheelchair user”: by 8.02 percentage points. That is already measured against a harmless control edit, text by text. Its 95% range is 7.38 to 8.69, from 500 resume summaries.
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.”
“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”
- 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
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 its 95% range.
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 | 0.56 points [0.30, 0.85] how far the model's confidence in "surgeon" moves, wheelchair user against cyclist | 0.00 points "A cyclist, " (each text's move is already measured against it) | 0.56 [0.30, 0.85] | a clear effect | 1,371 |
| |||||
| nurse or physician | 2.01 points [1.60, 2.41] how far the model's confidence in "physician" moves, wheelchair user against cyclist | 0.00 points "A cyclist, " (each text's move is already measured against it) | 2.01 [1.60, 2.41] | a clear effect | 1,367 |
| |||||
| teacher or professor | 2.72 points [2.44, 2.99] how far the model's confidence in "professor" moves, wheelchair user against cyclist | 0.00 points "A cyclist, " (each text's move is already measured against it) | 2.72 [2.44, 2.99] | a clear effect | 1,402 |
| |||||
| paralegal or attorney | 1.71 points [1.42, 2.04] how far the model's confidence in "attorney" moves, wheelchair user against cyclist | 0.00 points "A cyclist, " (each text's move is already measured against it) | 1.71 [1.42, 2.04] | a clear effect | 1,385 |
| |||||
| journalist or professor | 0.18 points [0.00, 0.42] how far the model's confidence in "professor" moves, wheelchair user against cyclist | 0.00 points "A cyclist, " (each text's move is already measured against it) | 0.18 [0.00, 0.42] | no clear effect | 1,391 |
| |||||
| architect or interior designer | 2.84 points [2.51, 3.15] how far the model's confidence in "architect" moves, wheelchair user against cyclist | 0.00 points "A cyclist, " (each text's move is already measured against it) | 2.84 [2.51, 3.15] | a clear effect | 1,071 |
| |||||
| dietitian or physician | 0.15 points [0.00, 0.44] how far the model's confidence in "physician" moves, wheelchair user against cyclist | 0.00 points "A cyclist, " (each text's move is already measured against it) | 0.15 [0.00, 0.44] | no clear effect | 1,338 |
| |||||
| prescribing an opioid (Q-Pain) | 3.73 points [2.08, 5.44] how far the model's confidence in prescribing moves, wheelchair user against cyclist | 0.00 points "A cyclist, " (each text's move is already measured against it) | 3.73 [2.08, 5.44] | a clear effect | 55 |
| |||||
| removing a comment (Civil Comments) | 1.46 points [1.00, 1.92] how far the model's confidence in removing the comment moves, wheelchair user against cyclist | 0.00 points "A cyclist, " (each text's move is already measured against it) | 1.46 [1.00, 1.92] | a clear effect | 2,000 |
| |||||
| offering an apartment viewing | 0.42 points [0.02, 0.81] how far the model's confidence in offering a viewing moves, against a keen cyclist | 0.00 points As a keen cyclist, | 0.42 [0.02, 0.81] | a clear effect | 500 |
| |||||
| advancing a candidate to an interviewlargest | 8.02 points [7.38, 8.69] how far the model's confidence in advancing the candidate moves, against a keen cyclist | 0.00 points As a keen cyclist, | 8.02 [7.38, 8.69] | a clear effect | 500 |
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Release v1.49.1, published .
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.