Literature DB >> 32723823

Algorithms as discrimination detectors.

Jon Kleinberg1, Jens Ludwig2, Sendhil Mullainathan3, Cass R Sunstein4.   

Abstract

Preventing discrimination requires that we have means of detecting it, and this can be enormously difficult when human beings are making the underlying decisions. As applied today, algorithms can increase the risk of discrimination. But as we argue here, algorithms by their nature require a far greater level of specificity than is usually possible with human decision making, and this specificity makes it possible to probe aspects of the decision in additional ways. With the right changes to legal and regulatory systems, algorithms can thus potentially make it easier to detect-and hence to help prevent-discrimination.

Entities:  

Keywords:  algorithms; discrimination; machine learning

Year:  2020        PMID: 32723823      PMCID: PMC7720101          DOI: 10.1073/pnas.1912790117

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   12.779


  5 in total

1.  A Dual-Self Model of Impulse Control.

Authors:  Drew Fudenberg; David K Levine
Journal:  Am Econ Rev       Date:  2006-12

2.  The Gender-Equality Paradox in Science, Technology, Engineering, and Mathematics Education.

Authors:  Gijsbert Stoet; David C Geary
Journal:  Psychol Sci       Date:  2018-02-14

3.  The effect of race and sex on physicians' recommendations for cardiac catheterization.

Authors:  K A Schulman; J A Berlin; W Harless; J F Kerner; S Sistrunk; B J Gersh; R Dubé; C K Taleghani; J E Burke; S Williams; J M Eisenberg; J J Escarce
Journal:  N Engl J Med       Date:  1999-02-25       Impact factor: 91.245

4.  HUMAN DECISIONS AND MACHINE PREDICTIONS.

Authors:  Jon Kleinberg; Himabindu Lakkaraju; Jure Leskovec; Jens Ludwig; Sendhil Mullainathan
Journal:  Q J Econ       Date:  2017-08-26

5.  Perceptions of race.

Authors:  Leda Cosmides; John Tooby; Robert Kurzban
Journal:  Trends Cogn Sci       Date:  2003-04       Impact factor: 20.229

  5 in total
  4 in total

1.  Measuring algorithmically infused societies.

Authors:  Claudia Wagner; Markus Strohmaier; Alexandra Olteanu; Emre Kıcıman; Noshir Contractor; Tina Eliassi-Rad
Journal:  Nature       Date:  2021-06-30       Impact factor: 49.962

2.  The science of deep learning.

Authors:  Richard Baraniuk; David Donoho; Matan Gavish
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-23       Impact factor: 11.205

Review 3.  Conceptualising fairness: three pillars for medical algorithms and health equity.

Authors:  Laura Sikstrom; Marta M Maslej; Katrina Hui; Zoe Findlay; Daniel Z Buchman; Sean L Hill
Journal:  BMJ Health Care Inform       Date:  2022-01

4.  Eliminating unintended bias in personalized policies using bias-eliminating adapted trees (BEAT).

Authors:  Eva Ascarza; Ayelet Israeli
Journal:  Proc Natl Acad Sci U S A       Date:  2022-03-08       Impact factor: 11.205

  4 in total

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