In authors or contributors

3 resources

  • Inioluwa Deborah Raji, Peggy Xu, Colleen...
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    Jun 9th, 2022
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    preprint
    Inioluwa Deborah Raji, Peggy Xu, Colleen...
    Jun 9th, 2022

    Much attention has focused on algorithmic audits and impact assessments to hold developers and users of algorithmic systems accountable. But existing algorithmic accountability policy approaches have neglected the lessons from non-algorithmic domains: notably, the importance of interventions that allow for the effective participation of third parties. Our paper synthesizes lessons from other fields on how to craft effective systems of external oversight for algorithmic deployments. First, we...

  • Rishi Bommasani, Drew A. Hudson, Ehsan A...
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    Dec 14th, 2021
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    journalArticle
    Rishi Bommasani, Drew A. Hudson, Ehsan A...
    Dec 14th, 2021

    AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical...

  • Rishi Bommasani, Drew A. Hudson, Ehsan A...
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    Jul 12th, 2022
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    preprint
    Rishi Bommasani, Drew A. Hudson, Ehsan A...
    Jul 12th, 2022

    AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical...

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