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Disentangling the Components of Ethical Research in Machine Learning
16 references·1 citing·40 second hop
Disentangling the Components of Ethical Research in Machine Learning (2022)Disentangling the Components of Ethical R…AI Ethics Statements (2022)AI Ethics StatementsTowards intellectual freedom in an AI Ethics Global Community (2021)Towards intellectual freedom …The steep cost of capture (2021)The steep cost of capturePre-registration: Weighing costs and benefits for researchers (2021)Pre-registration: Weighing co…Large image datasets: A pyrrhic win for computer vision? (2021)Large image datasets: A pyrrh…Institutionalizing ethics in AI through broader impact requirements (2021)Institutionalizing ethics in …Ethics and society review: Ethics reflection as a precondition to research funding (2021)Ethics and society review: Et…When not to design, build, or deploy (2020)When not to design, build, or…Model Cards for Model Reporting (2019)Model Cards for Model Reporti…Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass (2019)Ghost Work: How to Stop Silic…Book review: Shoshana Zuboff, The Age of Surveillance Capitalism: The Fight for Human Future at the New Frontier of Power (2019)Book review: Shoshana Zuboff,…Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (2018)Automating Inequality: How Hi…Algorithms of Oppression (2018)Algorithms of OppressionWhere are human subjects in Big Data research? The emerging ethics divide (2016)Where are human subjects in B…1,500 scientists lift the lid on reproducibility (2016)1,500 scientists lift the lid…Reproducible research can still be wrong: Adopting a prevention approach (2015)Reproducible research can sti…"Ethics is not neutral": Understanding Ethical and Responsible AI Design from the Lenses of Black Youth (2025)"Ethics is not neutral": Unde…Why Most Published Research Findings Are False (2005)Why Most Published Research Findings Are False (2005)Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences (2010)Identifying and Overcoming Threats to Reproducibility, Replicability, Robustness, and Generalizability in Microbiome Research (2018)Open science, reproducibility, and transparency in ecology (2018)Leakage and the reproducibility crisis in machine-learning-based science (2023)CRITICAL QUESTIONS FOR BIG DATA (2012)Big data: a revolution that will transform how we live, work, and think (2013)Experimental evidence of massive-scale emotional contagion through social networks (2014)Principles alone cannot guarantee ethical AI (2019)Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019)Fairness in Machine Learning: A Survey (2023)Estimating the reproducibility of psychological science (2015)A manifesto for reproducible science (2017)The nf-core framework for community-curated bioinformatics pipelines (2020)Sustainable data analysis with Snakemake (2021)Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement (2015)Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory, and Antiracist Politics [1989] (2018)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Closing the AI accountability gap (2020)A Survey on Bias and Fairness in Machine Learning (2021)Dilemmas in a general theory of planning (1973)Street-Level Bureaucracy: Dilemmas of the Individual in Public Services (1981)Algorithms at Work: The New Contested Terrain of Control (2019)Fairness and Abstraction in Sociotechnical Systems (2019)The Ethics of AI Ethics: An Evaluation of Guidelines (2020)Sorting Things Out (1999)Data and its (dis)contents: A survey of dataset development and use in machine learning research (2021)Multimodal datasets: misogyny, pornography, and malignant stereotypes (2021)The Values Encoded in Machine Learning Research (2022)On the Opportunities and Risks of Foundation Models (2021)Can HR adapt to the paradoxes of artificial intelligence? (2022)Artificial intelligence and illusions of understanding in scientific research (2024)Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making (2024)Consumers and Artificial Intelligence: An Experiential Perspective (2020)Data Feminism (2020)Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020)Forgetting Practices in the Data Sciences (2022)Power to the People? Opportunities and Challenges for Participatory AI (2022)Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks (2024)
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References

  • 20161,500 scientists lift the lid on reproducibility4,536 cited
  • 2018Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor3,318 cited
  • 2019Model Cards for Model Reporting3,049 cited
  • 2018Algorithms of Oppression2,778 cited
  • 2019Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass1,082 cited
  • 2016Where are human subjects in Big Data research? The emerging ethics divide402 cited
  • 2021Large image datasets: A pyrrhic win for computer vision?355 cited
  • 2015Reproducible research can still be wrong: Adopting a prevention approach193 cited
  • 2021The steep cost of capture165 cited
  • 2019Book review: Shoshana Zuboff, The Age of Surveillance Capitalism: The Fight for Human Future at the New Frontier of Power88 cited
  • 2021Pre-registration: Weighing costs and benefits for researchers74 cited
  • 2021Institutionalizing ethics in AI through broader impact requirements69 cited
  • 2021Ethics and society review: Ethics reflection as a precondition to research funding67 cited
  • 2021Towards intellectual freedom in an AI Ethics Global Community34 cited
  • 2020When not to design, build, or deploy31 cited

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  • 2022AI Ethics Statements24 cited
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    • 2025"Ethics is not neutral": Understanding Ethical and Responsible AI Design from the Lenses of Black Youth12 cited