AI incident #19 ·

Sexist and Racist Google Adsense Advertisements

Open on the AI Incident Database 27 news reports Synced from the AIID API · record last edited 5 Sep 2026

What happened

Advertisements chosen by Google Adsense are reported as producing sexist and racist results.

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 (27)

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

  1. Discrimination in Online Ad Delivery
    arxiv.org · Latanya Sweeney · AIID #160
  2. Discrimination in Online Ad Delivery
    dataprivacylab.org · Data Privacy Lab · AIID #158
  3. Embedded racism determines online advertising placement
    privacyinternational.org · Privacy International · AIID #168
  4. Google search results 'show racial bias'
    telegraph.co.uk · The Telegraph · AIID #179
  5. Racism is Poisoning Online Ad Delivery, Says Harvard Professor
    technologyreview.com · Will Knight, Douglas Heaven · AIID #182
  6. Google a 'Black' Name, Get an Arrest Ad?
    theroot.com · The Root Staff · AIID #187
  7. Can Googling be racist?
    theguardian.com · Arwa Mahdawi · AIID #176
  8. Study Finds Google Search Ads Are Racially Biased
    businessinsider.com · Agence France Presse · AIID #177
  9. Harvard professor spots Web search bias
    bostonglobe.com · Globe Staff · AIID #181
  10. Discrimination in Online Ad Delivery
    queue.acm.org · Latanya Sweeney · AIID #184
  11. How Much Does Your Name Matter?
    freakonomics.com · Freakonomics · AIID #161
  12. Can computers be racist? Big data, inequality, and discrimination
    fordfoundation.org · Michael Brennan, Judith Heumann, Moushira Elgeziri · AIID #172

Who was involved

Alleged deployer
Google
On AIID: Google
Alleged developer
Google
On AIID: Google
Alleged harmed party
Women and girls Women Minority groups
On AIID: Women and girls, Women, Minority groups

AI systems implicated

Google AdSense ad-selection system

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
none
Sectors
information and communication
Countries
US

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 1.1.

  • Discrimination

    "Discrimination - Unfair or inadequate treatment or arbitrary distinction based on a person’s race, ethnicity, age, gender, sexual preference, religion, national origin, marital status, disability, language, or other pro...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Harms of Representation and Other Biases

    "A pretrained LLM generally has many of the stereotypical biases commonly present in the human society (Touvron et al., 2023). This makes it difficult for users to trust that LLMs will work well for them and not produce...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Risks from bias and underrepresentation

    "The outputs and impacts of general- purpose AI systems can be biased with respect to various aspects of human identity, including race, gender, culture, age, and disability. This creates risks in high- stakes domains su...

    International Scientific Report on the Safety of Advanced AI (Bengio2024)

  • Bias

    "General-purpose AI systems can amplify social and political biases, causing concrete harm. They frequently display biases with respect to race, gender, culture, age, disability, political opinion, or other aspects of hu...

    International AI Safety Report 2025 (Bengio2025)

  • Bias

    "The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases"

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

  • Toxicity and Bias Tendencies

    "Extensive data collection in LLMs brings toxic content and stereotypical bias into the training data."

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

  • Biased Training Data

    "Compared with the definition of toxicity, the definition of bias is more subjective and contextdependent. Based on previous work [97], [101], we describe the bias as disparities that could raise demographic differences...

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

  • Broken systems

    "These are the most mentioned cases. They refer to situations where the algorithm or the training data lead to unreliable outputs. These systems frequently assign disproportionate weight to some variables, like race or g...

    Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

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