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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
3
Located 2 of 2 cases · 0 unknown
Languages in checked sources
2
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.

No known event years in this selection.

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2 of 1101 published cases

Event date unknownTaiwanUnidentified video tool

Taiwan police say a Taichung fraud call centre used an AI face-swap tool in video calls to win the trust of women in the US and Canada

Taiwanese police said in June 2025 that a fraud call centre in Shalu district, Taichung, had targeted overseas Chinese women in the United States and Canada with romance-investment fraud. According to the police account reported by SETN and carried by Yahoo News Taiwan on 4 June 2025, the call centre created a range of male personas, cultivated relationships with the women, and video-chatted with them through an 'AI face replacement tool' that swapped the operator's face for a persona's; because the picture was very realistic, police say, many victims believed it, after which they were talked into investing in fake energy products and sending money to accounts the suspects supplied. Police say analysis of seized phones identified seven women in the US and Canada who were defrauded, with losses of about NT$3.5 million so far, and that the number of victims and the amount were still being compiled. A photo caption in the report says officers demonstrated the face-replacement tool. The call centre was raided in November 2024, and nine suspects were referred to Taichung prosecutors, seven of them detained; no indictment or verdict has been found.

Core concern Medium reported severity Investigation Opened

AI involvement reported · Causal attribution alleged · 1 source · Added 29/10/2026

Event date unknownUnited Kingdom, GeorgiaUnidentified video tool

England: a man told the Guardian he lost £27,000 to a Tbilisi call-centre fraud after a deepfake video of broadcaster Ben Fogle led him to a crypto site

A man in his 30s from East Anglia, England, told the Guardian (pseudonym used) that he watched a video of broadcaster Ben Fogle apparently promoting cryptocurrency trading on a site called AdmiralsFX, entered his details, was called by what turned out to be a Tbilisi call centre, paid a £250 buy-in and within a month had handed over £27,000, about a year's salary. The Guardian describes the clip as a deepfake, a digitally doctored video Fogle had publicly warned about in spring 2024. The account was published in March 2025 as part of a Guardian, SVT and OCCRP investigation of a leak from the Georgian operation, which the Guardian says duped at least 6,179 people and whose victims frequently cited the deepfake ads as the reason they went ahead.

Core concern Medium reported severity Investigation Opened

AI involvement reported · Causal attribution alleged · 1 source · Added 09/10/2026

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: 11/11/2026. Dataset available under CC BY 4.0.