AI Policy & Governance, Privacy & Data
Ableism And Disability Discrimination In New Surveillance Technologies: How new surveillance technologies in education, policing, health care, and the workplace disproportionately harm disabled people
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Introduction
Algorithmic technologies are everywhere. At this very moment, you can be sure students around the world are complaining about homework, sharing gossip, and talking about politics — all while computer programs observe every web search they make and every social media post they create, sending information about their activities to school officials who might punish them for what they look at. Other things happening right now likely include:
- Delivery workers are trawling up and down streets near you while computer programs monitor their location and speed to optimize schedules, routes, and evaluate their performance;
- People working from home are looking at their computers while their computers are staring back at them, timing their bathroom breaks, recording their computer screens, and potentially listening to them through their microphones;
- Your neighbors – in your community or the next one over – are being tracked and designated by algorithms targeting police attention and resources to some neighborhoods but not others;
- Your own phone may be tracking data about your heart rate, blood oxygen level, steps walked, menstrual cycle, and diet, and that information might be going to for-profit companies or your employer. Your social media content might even be mined and used to diagnose a mental health disability.
This ubiquity of algorithmic technologies has pervaded every aspect of modern life, and the algorithms are improving. But while algorithmic technologies may become better at predicting which restaurants someone might like or which music a person might enjoy listening to, not all of their possible applications are benign, helpful, or just.
Scholars and advocates have demonstrated myriad harms that can arise from the types of encoded prejudices and self-perpetuating cycles of discrimination, bias, and oppression that may result from automated decision-makers. These potentially harmful technologies are routinely deployed by government entities, private enterprises, and individuals to make assessments and recommendations about everything from rental applications to hiring, allocation of medical resources, and whom to target with specific ads. They have been deployed in a variety of settings including education and the workplace, often with the goal of surveilling activities, habits, and efficiency.
Disabled people comprise one such community that experiences discrimination, bias, and oppression resulting from automated decision-making technology. Disabled people continually experience marginalization in society, especially those who belong to other marginalized communities such as disabled women of color. Yet, not enough scholars or researchers have addressed the specific harms and disproportionate negative impacts that surveillance and algorithmic tools can have on disabled people. This is in part because algorithmic technologies that are trained on data that already embeds ableist (or relatedly racist or sexist) outcomes will entrench and replicate the same ableist (and racial or gendered) bias in the computer system. For example, a tenant screening tool that considers rental applicants’ credit scores, past evictions, and criminal history may prevent poor people, survivors of domestic violence, and people of color from getting an apartment because they are disproportionately likely to have lower credit scores, past evictions, and criminal records due to biases in the credit and housing systems and in policing disparities.
This report examines four areas where algorithmic and/or surveillance technologies are used to surveil, control, discipline, and punish people, with particularly harmful impacts on disabled people. They include: (1) education; (2) the criminal legal system; (3) health care; and (4) the workplace. In each section, we describe several examples of technologies that can violate people’s privacy, contribute to or accelerate existing harm and discrimination, and undermine broader public policy objectives (such as public safety or academic integrity).
