What Is Neurodiversity?

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Biased hiring practices can be magnified and scaled by technology.

Using data collected in unfair systems to train algorithmic selection codifies bias.

Fairness in hiring must start with humans designing and establishing fairer systems, not with automation.

Applying for jobs is stressful for most people. And it does not help when you know that "people like you" are likely to have a gigantic minus placed on their application—increasingly, by a machine.

The automated screening process is frequently met with applicant suspicion (1, 2). And in many cases, for good reason—the process is indeed fraught with bias. As just one example, older applicants are now given advice on outsmarting ageist bias, which appears to be built into many automated applicant screening systems and AI-saturated hiring models, by removing or condensing information on earlier experience and education (2). Sadly, even if removing older positions will help one person get a job, it does nothing to address ageism. If anything, it further embeds the connection between “too much experience" and "unhireable” into the system—and the cycle of ageism continues.

The cycle of biased practices begetting more biased practices can be clearly illustrated by analyzing neuroexclusion at work.

Neurodivergence, measurement validity, and the irony of human bias

Many automated screening systems reproduce biases that exist in manual resume screening. However, expanded versions of automated screening may include additional barriers traceable to the lack of validity—the correspondence between measurement and what is supposedly being measured. One of the validity challenges here is that systems with built-in assessments, such as personality testing, are........

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