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Abeba Birhane

Abeba Birhane

    • Senior Advisory in AI Accountablilty - Mozilla Foundation 
    • Adjunct Assistant Professor - School of Computer Science and Statistics at Trinity College Dublin, Ireland 
    • Member of the Newly Formed UN High-Level Advisory Body on AI
Full Bio
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Apr 2023 -- Abeba Birhane -- Human Agency in the Age of Algorithms

Apr 2023 -- Abeba Birhane -- Human Agency in the Age of Algorithms

Algorithmic Injustices and Relational Ethics with Abeba Birhane

Algorithmic Injustices and Relational Ethics with Abeba Birhane

Abeba Birhane Speaker Biography

Abeba Birhane is a cognitive scientist, currently a Senior Advisor in AI Accountability at Mozilla Foundation and an Adjunct Assistant Professor at the School of Computer Science and Statistics at Trinity College Dublin, Ireland. She researches human behaviour, social systems, and responsible and ethical AI – work for which she was recently featured in Wired UK and TIME on the TIME100 Most Influential People in AI list.

Birhane’s work explores the challenges and pitfalls of automating human behaviour through critical examination of existing models and audits of large scale datasets used for training models. She demonstrated patterns of problematic data collection, labelling, and use of large scale image datasets like MIT’s 80 Million Tiny Images, which was taken down as a direct result of one of her audit papers. Her paper showed that these datasets – which were used to develop numerous algorithmic tools – encode racist, misogynist, and otherwise marginalising labels that cause downstream harms (as models trained on such dataset further exacerbate these problems), especially towards people at the margins of society.

Birhane was awarded the 2019 NeurIPS Black in AI Best Paper Award, a 2020 VentureBeat AI Innovations in Computer Vision Award, a 2022 Fairness Accountability and Transparency (FAccT) Distinguished Paper Award, and a 2022 Lego Director’s Prize for her PhD thesis contribution, among many other honours.

  • AI Ethics,
  • Societal implications of generative AI,
  • Data ethics,
  • Large scale datasets,
  • Surveillance AI,
  • Bias, Fairness, Critical cognitive science

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