Development and Validation of the Perceived Personal Algorithmic Harms Scale: Algorithmic Stigma, Algorithmic Privacy Harms, Tangible Algorithmic Harms, and Race, Gender, and Disability Differences

Algorithmic systems, including those using AI techniques, are increasingly deployed across domains from healthcare to law enforcement. Despite promises of societal good, these systems can inflict real harm to individuals and societies. Yet no validated scales exist to systematically measure individuals’ experiences and perceptions of algorithmic harm, which we refer to as perceived personal algorithmic harms . We develop and validate the Perceived Personal Algorithmic Harms Scale with a representative U.S. sample ( n = 742) and oversampling of people of color, disabled individuals, transgender, and nonbinary participants. The scale comprises three subscales – algorithmic stigma, algorithmic privacy harms, and tangible algorithmic harms – and can be used to measure personal algorithmic harms across systems, contexts, and populations. Our findings empirically confirm that algorithmic stigma and privacy harms, previously theorized, are experienced in measurable ways, with minoritized groups (transgender, nonbinary, mixed race, disabled, and/or neurodivergent people) reporting higher levels of harm than majority counterparts. The scale provides a versatile tool – including as a means for critical refusal and repair – for researchers, lawyers, policymakers, technologists, civil society, and the public to assess past, present, or anticipated harms, evaluate specific systems, and identify vulnerable groups prior to deployment. In sum, this work advances both theoretical understanding and practical intervention in addressing the lived impacts of algorithmic systems.