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The Limits of Value Transparency in Machine Learning
14 references·0 citing·41 second hop
The Limits of Value Transparency in Machine Learning (2022)The Limits of Value Transparency in Machi…Transparency is Surveillance (2021)Transparency is SurveillanceOn value-laden science (2020)On value-laden scienceOn Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning (2020)On Predicting Recidivism: Epi…A Taxonomy of Transparency in Science (2020)A Taxonomy of Transparency in…Understanding from Machine Learning Models (2019)Understanding from Machine Le…Problem Formulation and Fairness (2019)Problem Formulation and Fairn…Dissecting racial bias in an algorithm used to manage the health of populations (2019)Dissecting racial bias in an …A Tapestry of Values: An Introduction to Values in Science (2019)A Tapestry of Values: An Intr…The fallacy of inscrutability (2018)The fallacy of inscrutabilityWhy the Argument from Inductive Risk Doesn’t Justify Incorporating Non-Epistemic Values in Scientific Reasoning (2017)Why the Argument from Inducti…How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)How the machine ‘thinks’: Und…Is there an ethics of algorithms? (2010)Is there an ethics of algorit…Science, Policy, and the Value-Free Ideal (2009)Science, Policy, and the Valu…Bias in computer systems (1996)Bias in computer systemsThe Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory (1984)Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science (2018)Improving Fairness in Machine Learning Systems (2019)Algorithmic Bias in Education (2021)Why Machine Ethics? (2006)Bearing Account-able Witness to the Ethical Algorithmic System (2015)Governance of algorithms: options and limitations (2015)Regulating artificial intelligence and robotics: ethics by design in a digital society (2019)Big Data's Disparate Impact (2014)The Black Box Society (2015)From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (2021)Operationalising AI Governance through Ethics-Based Auditing: An Industry Case Study (2022)Technology, Megatrends and Work: Thoughts on the Future of Business Ethics (2022)The Social Construction of Facts and Artefacts: or How the Sociology of Science and the Sociology of Technology might Benefit Each Other (1984)Algorithmic bias: Senses, sources, solutions (2021)Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2021)The Fallacy of AI Functionality (2022)Inductive Risk and Values in Science (2000)Explanation and Understanding (2005)Extending ourselves: computational science, empiricism, and scientific method (2005)Reclaiming AI as a Theoretical Tool for Cognitive Science (2024)The Brain Abstracted (2024)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)Semantics derived automatically from language corpora contain human-like biases (2017)Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020)Addressing bias in big data and AI for health care: A call for open science (2021)Fairness through awareness (2012)The Relevance of Algorithms (2014)The ethics of algorithms: Mapping the debate (2016)Algorithms at Work: The New Contested Terrain of Control (2019)On the Opportunities and Risks of Foundation Models (2021)Trust and Antitrust (1986)Seeing like a State: How Certain Schemes to Improve the Human Condition Have Failed (1998)Epistemic Injustice (2007)AI, Opacity, and Personal Autonomy (2022)Varieties of transparency: exploring agency within AI systems (2022)The Grounds of Political Legitimacy (2023)Criteria for assessing grant applications: a systematic review (2020)Does democracy require value-neutral science? Analyzing the legitimacy of scientific information in the political sphere (2021)Constructing sustainability science: emerging perspectives and research trajectories (2012)The Practical Relevance of Management Research: Turning the Debate on Relevance into a Rigorous Scientific Research Program (2015)
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References

  • 2019Dissecting racial bias in an algorithm used to manage the health of populations7,086 cited
  • 2016How the machine ‘thinks’: Understanding opacity in machine learning algorithms3,179 cited
  • 2009Science, Policy, and the Value-Free Ideal1,397 cited
  • 1996Bias in computer systems1,203 cited
  • 2019Problem Formulation and Fairness283 cited
  • 2019Understanding from Machine Learning Models229 cited
  • 2010Is there an ethics of algorithms?170 cited
  • 2021Transparency is Surveillance135 cited
  • 2018The fallacy of inscrutability131 cited
  • 2019A Tapestry of Values: An Introduction to Values in Science96 cited
  • 2020A Taxonomy of Transparency in Science76 cited
  • 2020On value-laden science75 cited
  • 2020On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning66 cited
  • 2017Why the Argument from Inductive Risk Doesn’t Justify Incorporating Non-Epistemic Values in Scientific Reasoning23 cited

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