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AI incident #828 ·

Uruguayan TV Program Santo y Seña Uses a Deepfake of Political Candidate Yamandú Orsi Without His Consent

Open on the AI Incident Database 3 news reports Snapshot 7 Sep 2026

What happened

Uruguayan TV program Santo y Seña, hosted by Ignacio Álvarez, used AI to create a virtual representation of political candidate Yamandú Orsi, who declined an appearance. Nevertheless, without Orsi's permission, an AI-generated "Orsi" was shown alongside Andrés Ojeda, a rival candidate who appeared in person on the program.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (3)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Santo y Seña mostró una entrevista falsa de Orsi, creada con inteligencia artificial
    lr21.com.uy · LaRed21 · AIID #4244
  2. Convidado de piedra: Orsi no fue a “Santo y seña” y el programa usó IA para tenerlo
    montevideo.com.uy · Montevideo Portal · AIID #4242
  3. Santo y Seña recreó conversación con Orsi por inteligencia artificial; “Pacha” Sánchez y Blanca Rodríguez salen al cruce
    elobservador.com.uy · El Observador · AIID #4243

Who was involved

Alleged deployer
Santo Y Sena, Canal 4
Alleged harmed party
Yamandu Orsi, Uruguayan Electorate, Uruguayan General Public, Democratic Integrity, Journalistic Integrity

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
Human
Intent
Intentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 3.1.

  • Pursuing Consistent Context

    "LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Knowledge Gaps

    "Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Hallucinations

    "LLMs generate nonsensical, untruthful, and factual incorrect content"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Faithfulness Errors

    "The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Defective Decoding Process

    In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Factuality Errors

    "The LLM-generated content could contain inaccurate information" which is factually incorrect

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Noisy Training Data

    "Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Incidents in the same risk subdomain

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