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Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits
16 references·14 citing·41 second hop
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Stage)-Specific Tools (2025)The ``Who", ``What", and ``Ho…The four-fifths rule is not disparate impact: A woeful tale of epistemic trespassing in algorithmic fairness (2024)The four-fifths rule is not d…The ethico-politics of design toolkits: responsible AI tools, from big tech guidelines to feminist ideation cards (2024)The ethico-politics of design…Inherent Limitations of AI Fairness (2024)Inherent Limitations of AI Fa…In-Between Visuals and Visible: The Impacts of Text-to-Image Generative AI Tools on Digital Image-making Practices in the Global South (2024)In-Between Visuals and Visibl…Implications of Regulations on the Use of AI and Generative AI for Human-Centered Responsible Artificial Intelligence (2024)Implications of Regulations o…Bitacora: A Toolkit for Supporting NonProfits to Critically Reflect on Social Media Data Use (2024)Bitacora: A Toolkit for Suppo…A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations (2024)A Scoping 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(2009)Principles alone cannot guarantee ethical AI (2019)The Ethics of AI Ethics: An Evaluation of Guidelines (2020)Ethics of AI in Education: Towards a Community-Wide Framework (2021)Semantics derived automatically from language corpora contain human-like biases (2017)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023)Big Data's Disparate Impact (2014)Algorithmic bias: review, synthesis, and future research directions (2021)Taxonomy of Risks posed by Language Models (2022)Counterfactual Fairness (2017)Fairness in Criminal Justice Risk Assessments: The State of the Art (2018)AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias (2019)Algorithms at Work: The New Contested Terrain of Control (2019)When data is capital: Datafication, accumulation, and extraction (2019)What Do Platforms Do? Understanding the Gig Economy (2020)Toward a Tribal Critical Race Theory in Education (2005)“Once You’ve Blended the Cake, You Can’t Take the Parts Back to the Main Ingredients”: Black Gay and Bisexual Men’s Descriptions and Experiences of Intersectionality (2012)Best Practices in Conceptualizing and Measuring Social Class in Psychological Research (2012)Intersectionality and why it matters to global health (2018)Exploratory Research (2020)Positionality practices and dimensions of impact on equity research: A collaborative inquiry and call to the community (2021)
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References

  • 2018Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory, and Antiracist Politics7,270 cited
  • 2019The global landscape of AI ethics guidelines6,172 cited
  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 1998Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory, and Antiracist Politics5,673 cited
  • 2018Algorithms of Oppression2,709 cited
  • 2011Data preprocessing techniques for classification without discrimination1,304 cited
  • 2018Fairness definitions explained1,125 cited
  • 2019Improving Fairness in Machine Learning Systems776 cited
  • 2019Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries762 cited
  • 2016Big data preprocessing: methods and prospects714 cited
  • 2020When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions548 cited
  • 2019Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse534 cited
  • 2017Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data511 cited
  • 2017FA*IR497 cited
  • 2018

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Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
491 cited
  • 2020Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI435 cited
  • Cited by

    • 2022The Model Card Authoring Toolkit: Toward Community-centered, Deliberation-driven AI Design44 cited
    • 2024In-Between Visuals and Visible: The Impacts of Text-to-Image Generative AI Tools on Digital Image-making Practices in the Global South42 cited
    • 2024A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations40 cited
    • 2023“☑ Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms39 cited
    • 2024Inherent Limitations of AI Fairness37 cited
    • 2024The four-fifths rule is not disparate impact: A woeful tale of epistemic trespassing in algorithmic fairness25 cited
    • 2023A step toward building a unified framework for managing AI bias23 cited
    • 2023Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective21 cited
    • 2025The ``Who", ``What", and ``How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools20 cited
    • 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools19 cited
    • 2024Implications of Regulations on the Use of AI and Generative AI for Human-Centered Responsible Artificial Intelligence19 cited
    • 2024The ethico-politics of design toolkits: responsible AI tools, from big tech guidelines to feminist ideation cards17 cited
    • 2022The AI field needs translational Ethical AI research15 cited
    • 2024Bitacora: A Toolkit for Supporting NonProfits to Critically Reflect on Social Media Data Use1 cited