Skip to main content

NOPE · AI and people

AI incidents

Reports of AI-related harm and adverse experiences affecting people’s safety, wellbeing, rights and livelihoods. Explore what happened and the evidence available.

NOPE’s core concern is when AI communicates with a person, acts on their behalf, or depicts or impersonates them. The tracker is wider: it also records consequential decisions and claims about people. Each account is reviewed for publication; claims may remain uncorroborated or disputed. How we review and count cases

In this selection

Published cases
5
Countries with reported events
4
Located 5 of 5 cases · 0 unknown
Languages in checked sources
1
Recorded for 5 of 5 cases

1 case has no reviewed AI-to-person relation yet: 0 not yet reviewed and 1 reviewed as unknown. Show these cases

These figures describe the cases collected by NOPE. Coverage varies with discovery, reporting and available evidence. They do not estimate how often AI-related harm occurs.

Response counts currently use each case’s principal recorded outcome. Further proceedings may be described in its account.

Cases in this selection, counted once in their first known event year. A series may continue beyond that year. Reporting and collection dates are excluded. NOPE has searched recent events more thoroughly than earlier years, so bar heights also reflect collection effort.

Reported severity MediumLow
More filters: AI relation, use, setting, sources and responses
Clear filters

5 of 392 published cases

30 Mar 2026South AfricaSASSA eLife facial verification

South Africa: pensioners report repeated failures of the facial recognition step in SASSA's eLife certification portal, and SASSA reports disruptions and office queues

The South African Social Security Agency (SASSA) introduced an online eLife Certification (life certification) for grant beneficiaries that uses biometric verification through its electronic Know Your Client (eKYC) system. IOL reports the certification was implemented on 30 March 2026. SASSA says beneficiaries who do not complete life certification as directed may face payment delays or suspension. On 10 April 2026 SASSA apologised to beneficiaries who could not access the portal, said system glitches linked to interfaces with other departments had caused delays, disruptions and long queues at its offices, and said the problem was resolved. On 23 April 2026 IOL reported that a pensioner couple said they had tried the facial recognition option 22 times since 2 April without success, and that beneficiaries nationwide told IOL they could not complete the certification, citing failures with facial recognition and one-time PINs, with one pensioner also reporting a message that Home Affairs was not available to verify their particulars. A SASSA spokesperson said the portals work and that 13,644 (88%) of the 15,499 unique clients who accessed the online verification services by 16 April were verified, and IOL reports SASSA admitted the system has been working intermittently. In a May 2026 report on a parliamentary reply, IOL said SASSA stated that unsuccessful facial recognition attempts on online platforms were among the causes of non-verification (those beneficiaries are redirected to fingerprint checks at local offices) and that it had recorded 7,779 complaints linked to its electronic facial biometric system. The department attributed facial verification issues to poor lighting, unstable connectivity or missing biometric records at Home Affairs. Neither May report mentions the eLife portal, and IOL places the figures within a biometric verification rollout that it dates from September 2025. The reports do not say how many grants were suspended because of facial verification failures.

Contextual tracker case Low reported severity

AI involvement reported · Causal attribution alleged · 5 sources, 4 underlying accounts · Added 29/09/2026

27 Jan 2026United KingdomFacewatch facial recognition

London: shopper told to leave a Sainsbury's store after a Facewatch facial recognition alert, which Sainsbury's says concerned a different person

A shopper at the Sainsbury's store in Elephant and Castle, London, told PA that staff approached the shopper on 27 January, asked the shopper to leave and took the shopping. The BBC and LBC report that staff pointed to a sign about the store's Facewatch facial recognition system and told the shopper to contact Facewatch. Sainsbury's said the system had flagged a different person. Sainsbury's and Facewatch said a staff member approached the wrong customer, and Facewatch said it held no alert or record for the shopper. To confirm this, the shopper sent Facewatch a passport copy and a photo. Sainsbury's apologised, offered a 75 pound voucher and said store management would receive additional training. The shopper reports embarrassment and feeling like a criminal. Both companies attribute the error to store staff.

AI relation unknown Low reported severity

AI involvement reported · Causal attribution disputed · 6 sources, 1 underlying account · Added 29/09/2026

Apr 2025United StatesUnidentified facial recognition system

New York: man misidentified through facial recognition in an indecent exposure case arrested and jailed two days

Reporting from August 2025 says an NYPD facial recognition search of images from a February 2025 indecent exposure in Manhattan's Union Square produced a possible match to a man who did not fit the described suspect. The man was placed in a photo lineup, arrested in April 2025 and jailed for two days. Prosecutors dismissed the case in July 2025 after his public defenders showed he was misidentified. The man says the process of becoming a correctional officer 'kind of' froze after the arrest. The NYPD says it never arrests solely on a facial recognition match.

Contextual tracker case Medium reported severity

AI involvement reported · Causal attribution alleged · 3 sources · Added 29/09/2026

Event date unknownUnited StatesUnidentified image tool

Gilmer County, Georgia: a vendor who restocked drink machines in local schools used AI applications to turn ordinary photographs of students into child sexual abuse material; prosecutors identified more than 150 underage victims, he was convicted on 118 counts and sentenced on 2 September 2026 to 40 years in prison, and parents of eight students sued Pepsi entities for keeping him on the school route

According to the DeKalb County District Attorney's Office, which prosecuted the case as conflict prosecutor, the investigation began in December 2024 when a Gilmer High School student told a school resource officer that a vendor who refilled drink machines on campus had asked her through a social media app to send him pictures. The Gilmer County Sheriff's Office arrested him in January 2025 and searched his devices, car and home. Prosecutors say he downloaded photographs of minors from social media and used AI applications and bots to alter them so the children appeared nude or engaged in sexual activity, and that the material related to more than 150 underage victims in Georgia and other states, many of them Gilmer County students. A Cobb County senior judge found him guilty on 13 August 2026 of 118 counts of sexual exploitation of children and on 2 September 2026, after 19 victim impact statements, sentenced him to 60 years with 40 to serve in prison. Separately, the parents of eight female students aged 12 to 17 sued him, Pepsi Beverage Co. and Pepsi-Cola Sales and Distribution in a suit reported on 28 February 2025, alleging the companies reinstated him to the same school route after students complained that he was photographing them; USA Herald reported the negligence suit again on 26 September 2026 and said Pepsi had not yet responded publicly. The AI tools are not named and the court and status of the civil suit are not reported.

Core concern High reported severity Involving minors Criminal Charges

AI involvement supported · Causal attribution supported · 8 sources, 5 underlying accounts · Added 27/09/2026

24 Jul 2026 to 26 Jul 2026IndiaUnidentified facial recognition system

Delhi: the police facial-recognition system logged at least 25 people who were in Tihar, Mandoli or Rohini jails as present at the Jantar Mantar student protests (20 to 26 July 2026), with timestamps on 24 to 26 July, on a sworn list of 2,873 persons with 'criminal antecedents' that the Supreme Court allowed police to register a fresh FIR against (Indian Express investigation, 4 September 2026)

In an affidavit of 17 August 2026 before the Supreme Court of India, Delhi Police said its Facial Recognition System (FRS) had identified 2,873 people with criminal antecedents at the Jantar Mantar protests of 20 to 26 July 2026 (2,402 through its 'Crime Kundli' biometric database and 471 through other criminal records). On 1 September the Court quashed the FIRs against the student protesters but let the police proceed against the 2,873; the police say any action will follow field verification. The Indian Express checked the 205 listed people facing murder, attempted-murder, rape or child-sexual-offence charges against police, prison and court records and found that at least 25 of them (17 accused of murder, four of rape (two or three under POCSO; the Express's narrative and its list differ), four of attempted murder) were lodged in Delhi's Tihar, Mandoli or Rohini prisons when the system logged them at the protest site with timestamps on 24, 25 and 26 July; some had been in custody for years. Delhi Police told the newspaper that further verification of the 2,873 was pending and, in its affidavit, that no action is taken solely on a facial-recognition result and that field verification follows each match. The police disclosed in 2022, in reply to a Right to Information request, that it treats a match with an 80 per cent similarity score as positive. Whether any of the 25 has since been named in the fresh FIR or visited for verification is not reported.

Contextual tracker case Medium reported severity Investigation Opened

AI involvement reported · Causal attribution supported · 4 sources, 2 underlying accounts · Added 24/09/2026

Cases may have several effects and sources. Mixed accounts qualify when they include a reported harm or adverse experience. People are counted within individual cases where sources support a number; we do not publish a collection-wide total of distinct people.

A source’s existence, the experience it reports and AI’s causal role are separate questions. A lawsuit records allegations unless a subsequent finding establishes them.

Methodology and corrections · Subscribe via RSS · Suggest a case or correction · Find support

Last dataset update: 30/09/2026. Dataset available under CC BY 4.0.