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AI incident #186 ·

Algorithm Assessing Risk Faced by Victims of Gender Violence Misclassified Low-Risk Cases, Allegedly Leading to Homicide of Women and Children in Spain

Open on the AI Incident Database 7 news reports Synced from the AIID API · record last edited 5 Sep 2026

What happened

In Spain, the algorithm that assesses recidivism risk in gender violence, VioGén, has allegedly critically underestimated the level of risk in a series of cases that ended in homicide of women and children since its first deployment.

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 (7)

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

  1. Las asesinadas que denunciaron se valoraron como 'riesgo bajo o nulo'
    elmundo.es · Rafael J. Álvarez · AIID #1933
  2. Nuevo protocolo para valorar riesgo de víctimas de género
    rtve.es · RTVE.es · AIID #1788
  3. Validation and Calibration of the Spanish Police Intimate Partner Violence Risk Assessment System (VioGén)
    link.springer.com · Juan José López-Ossorio, José Luis González-Álvarez, José Manuel Muñoz Vicente · AIID #1592
  4. In Spain, the VioGén algorithm attempts to forecast gender violence
    algorithmwatch.org · Michele Catanzaro · AIID #1590
  5. The External Audit of the VioGen System
    eticasfoundation.org · Eticas Foundation · AIID #1591
  6. Las víctimas denuncian fallos en VioGén, el algoritmo contra la violencia de género
    eldiario.es · Carlos del Castillo · AIID #1934
  7. AI: Decoded: Spain’s flawed domestic abuse algorithm — Ban debate heats up — Holding the police accountable
    politico.eu · Melissa Heikkilä · AIID #1593

Who was involved

Alleged harmed party
Women and girls Women Victims of gender violence in Spain Victims of gender violence Victims of domestic abuse Minors General public of Spain General public
On AIID: Women and girls, Women, Victims of gender violence in Spain, Victims of gender violence, Victims of domestic abuse, Minors, General public of Spain, General public

AI systems implicated

VioGénAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

  • Reliability issues

    "Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...

    International AI Safety Report 2025 (Bengio2025)

  • Type 2: Bigger than expected

    Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Type 3: Worse than expected

    AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Ethics and Morality Issues

    LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Safe learning

    "AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."

    AGI Safety Literature Review (Everitt2018 )

  • Malign belief distributions

    "Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...

    AGI Safety Literature Review (Everitt2018 )

  • Meta-cognition

    "Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...

    AGI Safety Literature Review (Everitt2018 )

  • Misaligned consequentialist reasoning

    "As we think about even more intelligent and advanced AI assistants, perhaps outperforming humans on many cognitive tasks, the question of how humans can successfully control such an assistant looms large. To achieve the...

    The Ethics of Advanced AI Assistants (Gabriel2024)

Linked by AIID editors or by its text-similarity model.

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