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Values and inductive risk in machine learning modelling: the case of binary classification models
16 references·0 citing·42 second hop
Values and inductive risk in machine learning modelling: the case of binary classification models (2021)Values and inductive risk in machine lear…A Survey on Bias and Fairness in Machine Learning (2021)A Survey on Bias and Fairness…Transparency in Complex Computational Systems (2020)Transparency in Complex Compu…Understanding from Machine Learning Models (2019)Understanding from Machine Le…Identifying and Reducing Gender Bias in Word-Level Language Models (2019)Identifying and Reducing Gend…The cost of fairness in binary classification (2018)The cost of fairness in binar…The accuracy, fairness, and limits of predicting recidivism (2018)The accuracy, fairness, and l…Fairness definitions explained (2018)Fairness definitions explainedHow the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)How the machine ‘thinks’: Und…Critical data studies: An introduction (2016)Critical data studies: An int…Fairness Constraints: Mechanisms for Fair Classification (2015)Fairness Constraints: Mechani…Deep learning applications and challenges in big data analytics (2015)Deep learning applications an…Big Data's Disparate Impact (2014)Big Data's Disparate ImpactThe philosophical novelty of computer simulation methods (2008)The philosophical novelty of …Inductive Risk and Values in Science (2000)Inductive Risk and Values in …Valuation and Acceptance of Scientific Hypotheses (1956)Valuation and Acceptance of S…The Scientist Qua Scientist Makes Value Judgments (1953)The Scientist Qua Scientist M…Evidence, Pragmatics, and Justification (2002)Empiricism, Semantics, and Ontology (2011)Towards a feminist HCI methodology (2011)Ethical Implications and Accountability of Algorithms (2018)Aspects of Scientific Explanation and Other Essays in the Philosophy of Science. (1966)The Essential Tension (1977)Science as Social Knowledge (1990)Bias and values in scientific research (2009)Epistemic Trust in Science (2012)In defence of the value free ideal (2013)Representing and Intervening (1983)Why Most Published Research Findings Are False (2005)CRITICAL QUESTIONS FOR BIG DATA (2012)Broken data: Conceptualising data in an emerging world (2018)Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South (2020)Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates (2022)A META‐ANALYSIS OF THE PREDICTORS OF ADULT OFFENDER RECIDIVISM: WHAT WORKS!* (1996)Predicting relapse: A meta-analysis of sexual offender recidivism studies. (1998)The accuracy of recidivism risk assessments for sexual offenders: A meta-analysis of 118 prediction studies. (2009)From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019)Machine behaviour (2019)In AI we trust? Perceptions about automated decision-making by artificial intelligence (2020)Fairness through awareness (2012)Equality of Opportunity in Supervised Learning (2016)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)Bias in data‐driven artificial intelligence systems—An introductory survey (2020)Algorithmic Bias in Education (2021)Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023)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)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)Big Data technologies: A survey (2017)Machine learning on big data: Opportunities and challenges (2017)Machine Learning With Big Data: Challenges and Approaches (2017)Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment (2016)Fairness Beyond Disparate Treatment & Disparate Impact (2017)Algorithmic Fairness: Choices, Assumptions, and Definitions (2020)Dissecting racial bias in an algorithm used to manage the health of populations (2019)
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References

  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2016How the machine ‘thinks’: Understanding opacity in machine learning algorithms3,179 cited
  • 2014Big Data's Disparate Impact3,046 cited
  • 2015Deep learning applications and challenges in big data analytics2,597 cited
  • 2018Fairness definitions explained1,125 cited
  • 2018The accuracy, fairness, and limits of predicting recidivism1,071 cited
  • 2000Inductive Risk and Values in Science878 cited
  • 1953The Scientist Qua Scientist Makes Value Judgments720 cited
  • 2015Fairness Constraints: Mechanisms for Fair Classification644 cited
  • 2016Critical data studies: An introduction467 cited
  • 2008The philosophical novelty of computer simulation methods362 cited
  • 1956Valuation and Acceptance of Scientific Hypotheses242 cited
  • 2019Understanding from Machine Learning Models229 cited
  • 2019Identifying and Reducing Gender Bias in Word-Level Language Models219 cited
  • 2020Transparency in Complex Computational Systems193 cited
  • 2018The cost of fairness in binary classification

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