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Algorithmic Bias and Risk Assessments: Lessons from Practice
16 references·3 citing·39 second hop
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References

  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2019Fairness and Abstraction in Sociotechnical Systems1,621 cited
  • 2019Review of “Race After Technology: Abolitionist Tools for the New Jim Code”1,474 cited
  • 2004The responsibility gap: Ascribing responsibility for the actions of learning automata1,095 cited
  • 2021Algorithmic bias: Senses, sources, solutions274 cited
  • 2021Ethics-Based Auditing to Develop Trustworthy AI267 cited
  • 2022Operationalising AI Governance through Ethics-Based Auditing: An Industry Case Study130 cited
  • 2020Ethics of Artificial Intelligence88 cited
  • 2014More Than You Wanted to Know83 cited
  • 2021An Institutional View Of Algorithmic Impact Assessments64 cited
  • 2021Assembling Accountability: Algorithmic Impact Assessment for the Public Interest61 cited
  • 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI53 cited
  • 2022Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness51 cited
  • 2016Plausible Cause: Explanatory Standards in the Age of Powerful Machines

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37 cited
  • 2020Theory choice, non-epistemic values, and machine learning19 cited
  • Cited by

    • 2024A Framework for Assurance Audits of Algorithmic Systems16 cited
    • 2023Norms for Academic Writing in the Era of Advanced Artificial Intelligence15 cited
    • 2023Engaging engineering teams through moral imagination: a bottom-up approach for responsible innovation and ethical culture change in technology companies13 cited