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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 also records consequential decisions, claims and privacy harms involving AI. Each case needs a described connection between AI use and the harm, including private information recorded into or disclosed to an AI service. Each account is reviewed for publication; claims may remain uncorroborated or disputed. How we review and count cases

In this selection

Published cases
2
Countries with reported events
1
Located 2 of 2 cases · 0 unknown
Languages in checked sources
1
Recorded for 2 of 2 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.

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2 of 1099 published cases

Jul 2025IndiaPoshan Tracker face recognition

India: pregnant women and new mothers report being cut off from take-home rations when the Poshan Tracker app's face match failed

From 1 July 2025 India's Ministry of Women and Child Development made a facial recognition check in the government's Poshan Tracker app mandatory before pregnant women and nursing mothers receive take-home rations under the Integrated Child Development Services (ICDS) scheme; the app compares the woman's live face with her Aadhaar identity photograph. In July 2025 Decode reported from Delhi that a woman eight months pregnant returned to her Anganwadi centre for a third day trying to get the app to recognise her, and a worker said she gave food to a new mother the app would not recognise and recorded it by hand. In August 2025 the Karnataka Anganwadi workers' union told The South First that the faces of pregnant and nursing mothers change so they no longer match their registration images, and that some mothers leave without their rations. In March 2026 a Decode investigation supported by the Pulitzer Center reported that a mother in Jharkhand was cut off from supplementary nutrition ten weeks after giving birth because the software could not match her face to a photograph taken eight years earlier, that a pregnant woman in Bihar who was anaemic went four months without rations because the system could not match her to an older Aadhaar photograph, that one Bihar centre had removed three pregnant women and five nursing mothers whose faces no longer matched, and that a Jharkhand worker was told to remove at least four children whose eligibility depended on their mothers' failed face matches. Decode also reports failures earlier in the process, when women cannot receive a one-time password on an Aadhaar-linked phone. Government figures cited by Decode show 52.7% of eligible beneficiaries had received rations through facial recognition by 31 December 2025. A researcher who analysed the app for Decode found Google's ML Kit face-detection library in it; Google said ML Kit cannot recognise individuals, and the government has not disclosed who built the matching system. After workers' protests, a February 2026 app update let workers upload other identity documents for pregnant and nursing women, subject to a supervisor's approval. Decode reports the Bihar woman later gave birth to a healthy baby, while her name remained missing from the system.

Contextual tracker case Medium reported severity Internal Action

AI involvement reported · Causal attribution alleged · 4 sources · Added 02/11/2026

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.

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Last dataset update: 04/11/2026. Dataset available under CC BY 4.0.