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Machine learning’s limitations in avoiding automation of bias
12 references·1 citing·31 second hop
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this work references cited by second hop

References

  • 2014Big Data's Disparate Impact3,281 cited
  • 2016Equality of Opportunity in Supervised Learning2,733 cited
  • 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments2,270 cited
  • 2015Certifying and Removing Disparate Impact1,827 cited
  • 2013Learning Fair Representations1,155 cited
  • 2020Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI1,048 cited
  • 2008Discrimination-aware data mining688 cited
  • 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search394 cited
  • 2016A Confidence-Based Approach for Balancing Fairness and Accuracy163 cited
  • 2014A Neutrosophic Multi-Criteria Decision Making Method97 cited
  • 2007ADMINISTRATIVE OFFICE OF THE UNITED STATES COURTS70 cited
  • 2011Fairness Through Awareness55 cited

Cited by

  • 2023Social context of the issue of discriminatory algorithmic decision-making systems10 cited

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