“Precrime" the claim that biometric technology can foretell crime

Sara Fathallah writes in the Inquest: In prisons and jails across the United States, authorities now use automated systems to transcribe phone calls and visitation videos and to flag words or phrases deemed risky. For decades, correctional facilities recorded and reviewed calls manually, but AI-driven systems now allow authorities to scan millions of minutes of conversations in real time.

In the 2010s, prisons began using a biometric technology called voiceprinting, which identifies individuals based on the unique characteristics of their voices. It allows correctional facilities to identify who is speaking on any given call and to search for other calls featuring the same voice. Texas-based Securus Technologies, one of the largest providers of prison phone services in the United States, supplies sophisticated voiceprinting services to hundreds of correctional agencies.

There is no scientific consensus on the validity of automatic speaker recognition, and experts recommend exercising extreme caution when using voice recognition as evidence in court. Even Securus’s 2016 patent acknowledges that “each given person’s vocal tract characteristics actually vary in a number of ways depending on time of day, how much the person has been talking that day and how loud, whether or not the person has a cold,” and other factors. But prisons continue to collect voiceprints and build growing databases; at least 200,000 voiceprints have been stored thus far. Sometimes, prisons pressure incarcerated people to give up their voice samples by threatening a complete loss of communications privileges to those who decline. In other instances, they enroll incarcerated people in voice recognition programs without their knowledge or consent. New York alone, for example, had already enrolled 92 percent of its incarcerated population by 2019.

In some jurisdictions, voiceprinting systems can be used to identify both incarcerated people and the individuals who speak to them. As representatives from the Electronic Frontier Foundation point out, such technologies can potentially be used to “profile anyone who has a voice that crosses into a prison, including all the parents, children, lovers, and friends of incarcerated people.” Advocates are afraid that authorities might flag individuals who are in touch with multiple incarcerated people, searching for patterns and ways to crack down on prison organizing.

Today, a growing array of wearable technologies—ankle monitors, bracelets that measure blood alcohol levels, smartphones themselves—are used to track people at nearly every stage of the criminal legal process.

A new generation of compulsory biometric devices, however, pushes far into dystopian territory, raising questions about how much biological information the carceral state feels entitled to collect. Some of these tools, already being tested in U.S. jails and prisons, take the form of rigid wristbands that monitor heart rate, skin temperature, cortisol levels, and so-called “activity” or stress indicators. According to the ACLU, they represent “not just a privacy invasion but an assault on inherent human dignity and autonomy.”

In some research initiates, the data gathered by biometric devices is already being analyzed and operationalized. In Indiana, a team of computer scientists and developers at Purdue University utilized such data in 2020 to train an AI algorithm to predict recidivism. According to the team’s press release, the project—funded by the Department of Justice and conducted in collaboration with county-level corrections and law enforcement agencies—harvested data such as stress and heart rates via wearable bracelets and smartphones. The stated goal was to determine which physiological indicators are linked to an individual’s “risk of returning to their criminal behavior.”

But as scholar Brian Jefferson notes in Digitize and Punish, algorithms used for carceral means are not “simply mathematical objects” but rather “artifacts of governance designed to achieve specific objectives.” By focusing on internal, physiological states rather than structural conditions—such as access to housing, employment, health care or social support—these models dismiss decades of work investigating recidivism and its social and economic causes. Those causes, as AI researchers Os Keyes and Chelsea Barabas have noted, are already well understood. What remains unsettled is why emerging technologies continue to search for answers inside the body, rather than in the systems that shape people’s lives.

Across these examples, a shared pattern emerges: the encoding of the body as evidence, often without the knowledge, consent, or recourse of those involved. This process strips people of their autonomy, dignity, and right against self-incrimination. Whether through DNA, eye movements, or physiological indicators of stress, these systems recast human bodies as sites of suspicion, deception, threat, or risk. Rather than eliminating human bias, they redistribute and reinforce it.

“Crime prediction algorithms,” Ruha Benjamin aptly explains, “should more accurately be called crime production algorithms.” Biometric tools are likely to expand further across the criminal legal system as police departments, courts, and prisons increasingly turn to A.I.-driven surveillance and predictive technologies. These tools are being deployed most aggressively in communities that are already heavily policed and disproportionately criminalized. Preparing for—and resisting—this expansion requires a broader understanding of biometrics beyond facial recognition alone, including the many ways bodily data can be collected and put to use. Fighting to ban facial recognition is not enough; it must be part of the larger fight to stop carceral biometrics and advance digital abolition.

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