On January 9, 2020, Detroit police arrested Robert Williams at his home in Farmington Hills, Michigan, in front of his wife and daughters. In a first-person account, Williams said he had nothing to do with the watch theft under investigation and learned during interrogation that police had relied on an incorrect facial-recognition identification. The arrest shows how an investigative lead can become dangerous when people treat it as proof rather than test it independently.
What happened to Robert Williams
Williams’s account describes a police arrest at home over a watch-theft investigation, with his family present. He says he was not involved in the theft and learned during questioning that a facial-recognition search had pointed investigators toward him. The ACLU’s case materials say he spent about 30 hours in detention and describe alleged failures in Detroit Police Department policy and training at the time.
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That account is about more than a mistaken computer result. A face search can direct investigators to a person, but people then decide how much weight to give the result, what other evidence to seek, and whether to use identification procedures that may reinforce the initial suspicion. In Williams’s case, the available materials establish that the facial-recognition identification was incorrect; they do not establish that the search result alone explains every decision leading to his arrest.
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A facial-recognition search produces a lead for investigators to assess. It is not, by itself, proof that the person shown in a candidate image committed a crime. The U.S. Commission on Civil Rights has warned that a facial-recognition lead combined with a lineup may still be insufficient on its own to seek an arrest warrant.
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The risk is that an early lead can shape what happens next. If investigators regard a candidate as the suspect before they have independently checked the evidence, later steps can appear to confirm the original result without actually providing independent corroboration. A lineup, for example, is not necessarily an independent check if the face-search lead influenced how it was conducted or interpreted.
This does not mean every facial-recognition search is followed by a mistaken arrest, or that the search alone determines what police do. It means each step needs scrutiny: what the search actually returned, what other evidence supports the identification, whether that evidence was gathered independently, and how investigators documented their decisions.
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How the Williams and Woodruff cases differ
| Case | Sequence described in the available account | Outcome or reported status |
|---|---|---|
| Robert Williams | Williams said Detroit officers arrested him at his Farmington Hills home on January 9, 2020, over a watch-theft case. He said he learned during interrogation that police had relied on an incorrect facial-recognition identification. The ACLU case materials say he spent about 30 hours in detention. | The ACLU described alleged policy and training failures. Detroit’s 2024 settlement announcement included changes and an audit commitment, but the announcement does not establish that the audit is complete. |
| Porcha Woodruff | According to the Associated Press, police searched an image from gas-station video and a witness picked Woodruff from a lineup. Police acknowledged she was the wrong suspect. | Charges were dropped. AP reported in September 2025 that a judge had dismissed Woodruff’s civil-rights lawsuit in an August ruling and that her attorney planned an appeal. That report does not establish the lawsuit’s current appellate status. |
Woodruff’s case is a separate example of the risks, not evidence that her investigation followed the same path as Williams’s. The accounts describe different identification sequences, and their later legal outcomes are different.
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In a 2024 settlement announcement, the ACLU of Michigan said Detroit agreed not to conduct lineups based solely on facial-recognition leads. The department also agreed to audit cases dating back to 2017 in which the technology was used to obtain an arrest warrant.
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The announcement describes commitments, not proof that the audit has been completed or that every safeguard is followed in practice. It also does not establish the latest operating policy beyond the terms announced in 2024. Those limits matter: a written rule can reduce a particular risk, but its effect depends on implementation, training, documentation, and oversight.
Why accuracy statistics cannot predict an individual arrest
NIST explains that false positives—incorrect matches—can contribute to questioning, surveillance, erroneous benefit decisions, or loss of liberty. But a system’s measured performance depends on the task and conditions being tested. A benchmark result is not the probability that a particular person arrested after a search is innocent, nor does it establish the error rate of the system used in Williams’s case.
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For a real investigation, relevant questions include the quality and conditions of the images, the search task, how candidates were reviewed, whether performance varied across demographic groups, what corroborating evidence existed, and whether a face-search result shaped a later lineup. The materials cited here do not establish a case-specific error rate for Williams’s search or provide a complete current comparison of facial-recognition systems.
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Safeguards that address the failure points
- Treat the result as a lead: Investigators should not describe a candidate match as a confirmed identification.
- Seek independent corroboration: Evidence should support the suspect’s identity without simply repeating or inheriting the face-search result.
- Protect identification procedures: A lineup should not be based solely on a facial-recognition lead, consistent with the restriction described in Detroit’s 2024 settlement announcement.
- Document and train: Agencies need clear rules for conducting and recording searches, interpreting results, and handling uncertainty; the ACLU’s Williams case materials raised allegations about policy and training failures at the time.
- Audit use and outcomes: Reviewing searches and resulting warrants can help identify whether safeguards are being applied. Detroit’s announced audit covers cases since 2017 in which the technology was used to obtain an arrest warrant; its completion is not established by the announcement.
ACLU of Michigan’s settlement announcement quoted Nathan Freed Wessler, deputy director of the ACLU Speech, Privacy, and Technology Project, saying: “The multiple wrongful arrests by police in Detroit and other American cities show that face recognition technology is fundamentally dangerous in the hands of law enforcement.” Williams’s and Woodruff’s cases illustrate why the crucial question is not only whether software can find a similar face, but whether investigators and agencies prevent a lead from becoming an untested basis for taking someone’s liberty.
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