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Law and Artificial Intelligence pp 109–134Cite as

Diversity and Inclusion in Artificial Intelligence

Part of the Information Technology and Law Series book series (ITLS,volume 35)

Abstract

Discrimination and bias are inherent problems of many AI applications, as seen in, for instance, face recognition systems not recognizing dark-skinned women and content moderator tools silencing drag queens online. These outcomes may derive from limited datasets that do not fully represent society as a whole or from the AI scientific community's western-male configuration bias. Although being a pressing issue, understanding how AI systems can replicate and amplify inequalities and injustice among underrepresented communities is still in its infancy in social science and technical communities. This chapter contributes to filling this gap by exploring the research question: what do diversity and inclusion mean in the context of AI? This chapter reviews the literature on diversity and inclusion in AI to unearth the underpinnings of the topic and identify key concepts, research gaps, and evidence sources to inform practice and policymaking in this area. Here, attention is directed to three different levels of the AI development process: the technical, the community, and the target user level. The latter is expanded upon, providing concrete examples of usually overlooked communities in the development of AI, such as women, the LGBTQ+ community, senior citizens, and disabled persons. Sex and gender diversity considerations emerge as the most at risk in AI applications and practices and thus are the focus here. To help mitigate the risks that missing sex and gender considerations in AI could pose for society, this chapter closes with proposing gendering algorithms, more diverse design teams, and more inclusive and explicit guiding policies. Overall, this chapter argues that by integrating diversity and inclusion considerations, AI systems can be created to be more attuned to all-inclusive societal needs, respect fundamental rights, and represent contemporary values in modern societies.

Keywords

  • Artificial Intelligence
  • Gender
  • Diversity
  • Inclusion
  • LGBT
  • AI Act

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Notes

  1. 1.

    Schönberger 2019; Wisskirchen et al. 2017; Righetti et al. 2019.

  2. 2.

    Noble 2018; Raji and Buolamwin 2019; Fosch-Villaronga et al. 2021.

  3. 3.

    Raji and Buolamwini 2019; Gomes et al. 2019.

  4. 4.

    Zhao et al. 2017.

  5. 5.

    Roopaei et al. 2021.

  6. 6.

    Willson 2017; Noble 2018; Ito 2019.

  7. 7.

    Liu 2021; Giger et al. 2019.

  8. 8.

    Danielescu 2020.

  9. 9.

    West et al. 2019; Rahman and Billionniere 2021.

  10. 10.

    Cirillo et al. 2020.

  11. 11.

    Park and Woo 2019.

  12. 12.

    Richardson 2016.

  13. 13.

    Schiebinger 2014; Tannenbaum et al. 2019.

  14. 14.

    Ekmekcioglu 2021.

  15. 15.

    Groom 2021.

  16. 16.

    Freireç et al. 2020.

  17. 17.

    See Article 5 of the Convention on the Elimination of All Forms of Discrimination against Women and Article 8(1)(b) of the Convention on the Rights of Persons with Disabilities.

  18. 18.

    Cirillo et al. 2020.

  19. 19.

    See Lexico’s definition at https://www.lexico.com/definition/diversity.

  20. 20.

    Mitchell et al. 2020.

  21. 21.

    O’Neil 2016; Caliskan et al. 2017.

  22. 22.

    Fosch-Villaronga et al. 2021.

  23. 23.

    See https://www.legislation.govt.nz/act/public/1995/0016/latest/DLM359369.html.

  24. 24.

    See https://www.acon.org.au/wp-content/uploads/2019/07/TGD_Language-Guide.pdf.

  25. 25.

    See https://www.glaad.org/reference/transgender.

  26. 26.

    Ibidem.

  27. 27.

    Fosch-Villaronga et al. 2021.

  28. 28.

    Custers 2013.

  29. 29.

    O’Neil 2016; Hamidi et al. 2018.

  30. 30.

    See ​​https://www.ohchr.org/en/issues/women/wrgs/pages/genderstereotypes.aspx.

  31. 31.

    Sink et al. 2018.

  32. 32.

    Burdge 2007; Howansky et al. 2019.

  33. 33.

    Freire et al. 2020.

  34. 34.

    See https://www.journals.elsevier.com/artificial-intelligence/editorial-board.

  35. 35.

    Cech and Waidzunas 2021; Tao 2018.

  36. 36.

    Whisnant 2012.

  37. 37.

    Gibney 2019.

  38. 38.

    Rathenau Institute 2021.

  39. 39.

    Schiebinger 2014.

  40. 40.

    Poulsen et al. 2020.

  41. 41.

    Nature Editorial 2018.

  42. 42.

    Haraway 2006; Bray 2007; Wajcman 2007.

  43. 43.

    Faulkner 2001.

  44. 44.

    O'Riordan and Phillips 2007.

  45. 45.

    Oudshoorn and Pitch 2003.

  46. 46.

    Page 2009.

  47. 47.

    Vida 2020.

  48. 48.

    Oudshoorn et al. 2004.

  49. 49.

    MoMa 2021.

  50. 50.

    Moscoso-Porras 2019.

  51. 51.

    Whittaker et al. 2019.

  52. 52.

    Bragg et al. 2019.

  53. 53.

    Goggin and Newell 2003.

  54. 54.

    United Nations 1993.

  55. 55.

    Temmerman et al. 2014.

  56. 56.

    Maxwell et al. 2006; Roussel 2013.

  57. 57.

    Fosch-Villaronga and Poulsen 2021.

  58. 58.

    Cech and Waidzunas 2021.

  59. 59.

    Gomes et al. 2019.

  60. 60.

    Strengers and Kennedy 2020.

  61. 61.

    See ​​https://www.gatebox.ai/en/hikari.

  62. 62.

    Liu 2021.

  63. 63.

    See ​​https://www.gatebox.ai/en/hikari.

  64. 64.

    Liu 2021.

  65. 65.

    Giger et al. 2019.

  66. 66.

    See https://clubfirst.org/product/sona-2-5-covid-19-robot/.

  67. 67.

    See http://www.xiaoice.com/.

  68. 68.

    See https://www.apple.com/au/siri/.

  69. 69.

    See https://assistant.google.com/.

  70. 70.

    Liu 2021.

  71. 71.

    Strengers and Kennedy 2020.

  72. 72.

    See India Times 2020 Covid-19: Jaipur Hospital Turns To Robots To Take Care Of Coronavirus Patients https://navbharattimes.indiatimes.com/video/news/covid-19-jaipur-hospital-turns-to-robots-to-take-care-of-coronavirus-patients/videoshow/74818092.cms.

  73. 73.

    Fosch-Villaronga et al. 2021.

  74. 74.

    Park and Woo 2019.

  75. 75.

    Park and Woo 2019.

  76. 76.

    Nosek et al. 2002a; Caliskan et al. 2017; Nosek et al. 2002b.

  77. 77.

    Zhao et al. 2017.

  78. 78.

    Zhao et al. 2017.

  79. 79.

    Sun et al. 2019; Zhou et al. 2019.

  80. 80.

    Campa et al. 2019.

  81. 81.

    Dupré et al. 2020; Fosch-Villaronga et al. 2021.

  82. 82.

    Buolamwini and Gebru 2018; Font and Costa-jussà 2019; McDuff et al. 2019; Torralba and Efros 2011.

  83. 83.

    Hamidi et al. 2018.

  84. 84.

    Keyes 2018; Fosch-Villaronga et al. 2021.

  85. 85.

    Fosch-Villaronga et al. 2021.

  86. 86.

    Fosch-Villaronga 2019a, b.

  87. 87.

    Hamidi et al. 2018; Büchi et al. 2020; Nišević et al. 2021.

  88. 88.

    Yu et al. 2018; Ahuja 2019.

  89. 89.

    Cirillo et al. 2020.

  90. 90.

    Topol 2019.

  91. 91.

    Esteva et al. 2017.

  92. 92.

    Wapner 2018.

  93. 93.

    Topol 2019.

  94. 94.

    Fosch-Villaronga et al. 2021.

  95. 95.

    Kamiran et al. 2013; Cirillo et al. 2020.

  96. 96.

    Fosch-Villaronga and Poulsen 2021.

  97. 97.

    Fosch-Villaronga and Poulsen 2021.

  98. 98.

    Jecker 2020.

  99. 99.

    Richardson 2016.

  100. 100.

    Scheutz and Arnold 2016.

  101. 101.

    Scheutz and Arnold 2016.

  102. 102.

    Fosch-Villaronga and Poulsen 2021.

  103. 103.

    Fosch-Villaronga and Poulsen 2021.

  104. 104.

    Behrendt 2018.

  105. 105.

    Zara et al. 2021.

  106. 106.

    Cirillo et al. 2020.

  107. 107.

    McGregor 2016.

  108. 108.

    Cirillo et al. 2020.

  109. 109.

    Jenkins et al. 2016.

  110. 110.

    Keyes 2018.

  111. 111.

    Kamiran et al. 2013.

  112. 112.

    Geyik et al. 2019.

  113. 113.

    Sommers 2006; Rock and Grant 2016.

  114. 114.

    Díaz-García et al. 2013.

  115. 115.

    Phillips et al. 2009.

  116. 116.

    Friedman and Hendry 2019; Friedman et al. 2006.

  117. 117.

    Poulsen et al. 2020.

  118. 118.

    CSIRO 2019.

  119. 119.

    Queer in AI 2019.

  120. 120.

    Jenkins 2016.

  121. 121.

    Keyes 2018.

  122. 122.

    Friedman and Hendry 2019; Friedman et al. 2006.

  123. 123.

    Fosch-Villaronga and Özcan 2019.

  124. 124.

    Carr 2011.

  125. 125.

    Jobin et al. 2019.

  126. 126.

    Martinetti et al. 2021. See Regulation (EU) of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regards to the processing of personal data and on the free movement of such data; Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices; and the Directive 2006/42/EC of the European Parliament and of the Council of 17 May 2006 on machinery.

  127. 127.

    AI Act 2021.

  128. 128.

    Stahl and Coeckelbergh 2016.

  129. 129.

    European Commission 2012.

  130. 130.

    Raji and Buolamwini 2019; Gomes et al. 2019.

  131. 131.

    Zhao et al. 2017.

  132. 132.

    Roopaei et al. 2021.

  133. 133.

    Mitchell et al. 2020.

  134. 134.

    Raji and Buolamwini 2019.

  135. 135.

    Willson 2017; Noble 2018; Ito 2019.

  136. 136.

    Some initiatives have started to explore these topics in the Netherlands. Check for instance the ‘Gendering Algorithms’ initiative started at Leiden University (see https://www.genderingalgorithms.org/) or the ‘Diversity and Inclusion for Embodied AI’ initiative started by the 4TU Federation and Leiden University (see https://www.dei4eai.com/).

  137. 137.

    Mitchell et al. 2020.

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  1. eLaw Center for Law and Digital Technologies, Leiden University, Leiden, The Netherlands

    Eduard Fosch-Villaronga

  2. Charles Sturt University, Bathurst, New South Wales, Australia

    Adam Poulsen

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    Dr. Bart Custers

  2. eLaw - Center for Law and Digital Technologies, Leiden University, Leiden, The Netherlands

    Dr. Eduard Fosch-Villaronga

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Fosch-Villaronga, E., Poulsen, A. (2022). Diversity and Inclusion in Artificial Intelligence. In: Custers, B., Fosch-Villaronga, E. (eds) Law and Artificial Intelligence. Information Technology and Law Series, vol 35. T.M.C. Asser Press, The Hague. https://doi.org/10.1007/978-94-6265-523-2_6

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