Abeba Birhane

Abeba Birhane

Senior Fellow in Trustworthy AI at Mozilla Foundation | Adjunct Lecturer/Assistant Professor at Trinity College Dublin

    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 also serves on the United Nations Secretary-General’s AI Advisory Body and the newly-convened AI Advisory Council in Ireland.

    All Sessions by Abeba Birhane

    Day 3 04/25/2024
    9:00 am - 9:25 am

    Social and Ethical Implications of Generative AI

    As Artificial Intelligence systems pervade day-to-day life, the need for these systems to be robust, fair, accurate, and just has become of urgent importance. As the foundational backbone of AI systems, large scale datasets play a crucial role in the performance, accuracy, robustness, fairness and trustworthiness of AI systems. In this talk, I: a) present work that highlights numerous concerns arising from large scale datasets, b) discuss the downstream impact of such dataset on models (including the exacerbation of societal biases and negative stereotypes) and c) review some approaches to both incremental improvements as well as shepherding broader structural change.

    Day 3 04/25/2024
    9:00 am - 9:25 am

    Social and Ethical Implications of Generative AI

    As Artificial Intelligence systems pervade day-to-day life, the need for these systems to be robust, fair, accurate, and just has become of urgent importance. As the foundational backbone of AI systems, large scale datasets play a crucial role in the performance, accuracy, robustness, fairness and trustworthiness of AI systems. In this talk, I: a) present work that highlights numerous concerns arising from large scale datasets, b) discuss the downstream impact of such dataset on models (including the exacerbation of societal biases and negative stereotypes) and c) review some approaches to both incremental improvements as well as shepherding broader structural change.

    Open Data Science

     

     

     

    Open Data Science
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