概要: Scammers are reportedly harnessing AI-generated deepfakes of health experts and public figures in Australia in order to sell health supplements and give harmful health advice. Among the reported cases, deepfake videos are alleged to have falsely depicted Jonathan Shaw and Karl Stefanovic endorsing "Glyco Balance" for diabetes management, while Karl Kruszelnicki reportedly was falsely shown promoting blood pressure pills.
Editor Notes: See also Incident 1076 for a variant of this incident ID, along with more information. Incidents 880 and Incident 1076 are closely related. Reconstructing the timeline of events for this ID: (1) November 2023: A fabricated interview transcript, allegedly attributed to Prof. Jonathan Shaw, is published online. The transcript reportedly claims he endorsed "Glyco Balance" as a breakthrough treatment for type 2 diabetes, and (2) AI-generated videos allegedly featuring Shaw and television host Karl Stefanovic appear on Facebook, promoting "Glyco Balance" with claims that reportedly misrepresent the effectiveness of the supplement and criticize established medications like metformin. (3) Earlier in 2024: Similar AI technology is reportedly used to create deepfake videos of Dr. Karl Kruszelnicki endorsing unproven blood pressure medications. These ads are widely circulated on Facebook and Instagram. (4) November 29, 2024: Meta removes the deepfake video of Shaw following a complaint from the Baker Institute. The removal reportedly required the filing of an intellectual property infringement claim. (5) December 9, 2024: The Australian Broadcasting Corporation publishes a report detailing these incidents.
Alleged: Unknown deepfake technology developer developed an AI system deployed by scammers, which harmed Karl Stefanovic , Karl Kruszelnicki , Jonathan Shaw , General public と Diabetes patients.
関与が疑われるAIシステム: Unknown deepfake app
インシデントのステータス
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
4.3. Fraud, scams, and targeted manipulation
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- Malicious Actors & Misuse
Entity
Which, if any, entity is presented as the main cause of the risk
Human
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Intentional
インシデントレポート
レポートタイムライン
糖尿病の著名な専門家は、薬を処方する人々を「バカ」と評するAI生成の動画を患者が見た後、患者に正しい薬を飲んでいると安心させざるを得なくなった。
メルボルンのベイカー心臓・糖尿病研究所の副所長ジョナサン・ショー氏は、フェイスブックで宣伝している新しい治療法について患者がクリニックに電話をかけてきて情報を求め始めたときに、この動画の存在を知った。
「リンクをクリックすると、もちろん私そっくりだったが、言葉がすべて変えられていた」とショー教授は語った。
「私が言うことを信じてしま…
バリアント
「バリアント」は既存のAIインシデントと同じ原因要素を共有し、同様な被害を引き起こし、同じ知的システムを含んだインシデントです。バリアントは完全に独立したインシデントとしてインデックスするのではなく、データベースに最初に投稿された同様なインシデントの元にインシデントのバリエーションとして一覧します。インシデントデータベースの他の投稿タイプとは違い、バリアントではインシデントデータベース以外の根拠のレポートは要求されません。詳細についてはこの研究論文を参照してください
