
"Bias-free" hiring AI that secretly screens out autistic candidates. That's the allegation in the ACLU's 2024 FTC complaint against Aon.
Here's what happened — and why every company using AI hiring tools should pay attention.
Aon marketed three assessment tools as fair and diversity-friendly. But the ACLU found that traits being scored — like "liveliness," "emotional awareness," and "positivity" — overlap directly with clinical diagnostic criteria for autism and mental health conditions.
In other words: the algorithm wasn't measuring job performance. It was measuring how well someone performs neurotypicality.
The tools penalized reserved communication, preference for routine, and flat vocal tone — all common autistic traits that have zero bearing on whether someone can do the job.
This isn't just an Aon problem. It exposes a structural flaw in how most AI hiring platforms work today.
Most are surface-level systems trained on historical data that already reflects decades of bias. They can't distinguish between genuine job qualifications and behavioral patterns that simply correlate with disability status.
Our research breaks down the full technical architecture — how adaptive personality tests, video interview scoring, and gamified cognitive assessments each create separate pathways for discrimination.
We also map out what actually works: causal modeling that mathematically isolates protected attributes from hiring decisions, adversarial training that forces algorithms to unlearn biased patterns, and neuro-inclusive design that adapts to individual baselines instead of comparing everyone to a neurotypical average.
The regulatory landscape is shifting fast. The FTC, EEOC, and state-level laws are moving from voluntary guidelines to active enforcement. "We didn't know" is no longer a defense.
Save this if your org uses any AI in recruitment — or send it to someone who needs to see it.
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