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Machine learning’s limitations in avoiding automation of bias · Study

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ArticleMachine learning’s limitations in avoiding automation of bias

Daniel Varona2020

Article2020

Machine learning’s limitations in avoiding automation of bias

Daniel Varona · Yadira Lizama-Mué · Juan Luis Suárez

27 citations·0/0 files stored·AI & Society

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Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI)Artificial Intelligence in Law

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References

2014Big Data's Disparate Impact 2016Equality of Opportunity in Supervised Learning 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2015Certifying and Removing Disparate Impact 2013Learning Fair Representations 2020Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI 2008Discrimination-aware data mining 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

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2023Social context of the issue of discriminatory algorithmic decision-making systems

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2023Fairness in Machine Learning: A Surveyarticle · 7 shared references 2023On Fairness and Calibrationchapter · 7 shared references 2020Review of Mathematical frameworks for Fairness in Machine Learningpreprint · 7 shared references 2017Algorithmic Decision Making and the Cost of Fairnessother · 6 shared references 2017Fairness Beyond Disparate Treatment & Disparate Impactother · 6 shared references 2018The Measure and Mismeasure of Fairnesspreprint · 6 shared references

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Year2020

Typearticle

Published2020-06-01

VenueAI & Society

ISSN-L0951-5666

Languageen

Citations27

Files0 public / 0

ShelfEthics and Social Impacts of AI

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doi10.1007/s00146-020-00996-y

openalex_workW3033166993

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