Diversity, Equity, and Inclusion in Artificial Intelligence: An Evaluation of Guidelines

References

• Accenture. 2019. Responsible AI and robotics: An ethical framework. Accenture, UK.
   Google Scholar
• ACM. 2017. Statement on algorithmic transparency and accountability. Washington, DC: Association for Computing Machinery US Public Policy Council.
   Google Scholar
• Avila, R., A. Brandusescu, J. O. Freuler, and D. Takur. 2018. Artifcial intelligence: Open questions about gender inclusion. Argentina: World Wide Web Foundation.
   Google Scholar
• Beijing Academy of Artificial Intelligence. 2019. Beijing AI Principles. Beijing Academy of Artificial Intelligence (BAAI), Beijing: Beijing Academy of Artifcial Intelligence.
   Google Scholar
• Benessaieh, K. 2022. Le Québec se classe 7e au monde. La Presse, 29(2): March 9th.
   Google Scholar
• Bolukbasi, T., K. W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai. 2016. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in Neural Information Processing Systems 29:4349–744.
   Google Scholar
• Bowen, D. E., and C. Ostroff. 2004. Understanding HRM–firm performance linkages: The role of the “strength” of the HRM system. Academy of Management Review 29 (2):203–21.
   Web of Science ®Google Scholar
• Boxenbaum, E. 2006. Lost in translation, the making of Danish diversity management. The American Behavioral Scientist 49 (7):939–48. doi:10.1177/0002764205285173.
   Web of Science ®Google Scholar
• Bradley, T. 2017 (July, 31). Facebook AI Creates Its Own Language in Creepy Preview of Our Potential Future. Forbes. https://www.forbes.com/sites/tonybradley/2017/07/31/facebook-ai-creates-its-own-language-in-creepy-preview-of-our-potential-future/?sh=4367685f292c
   Google Scholar
• Cachat-rosset, G., K. A. Carillo, and A. Klarsfeld. 2019. Reconstructing the concept of diversity climate–a critical review of its definition, dimensions, and operationalization. European Management Review 16 (4):863–85. doi:10.1111/emre.12133.
   Web of Science ®Google Scholar
• Cachat-rosset, G., K. A. Carillo, and A. Klarsfeld. 2021. Exploring the impact of diversity climate on individual work role performance: A novel approach. European Management Review 19 (2):248–62. doi:10.1111/emre.12483.
   Web of Science ®Google Scholar
• Campolo, A., M. Sanflippo, M. Whittaker, and K. Crawford 2017. AI Now Report 2017. AI Now Institute, New York.
   Google Scholar
• Chung, Y., H. Liao, S. E. Jackson, M. Subramony, S. Colakoglu, and Y. Jiang. 2015. Cracking but not breaking: Joint effects of faultline strength and diversity climate on loyal behavior. Academy of Management Journal 58 (5):1495–515. doi:10.5465/amj.2011.0829.
   Web of Science ®Google Scholar
• COMEST/UNESCO. 2017. Report of COMEST on robotics ethics. COMEST/UNESCO SHS/YES/COMEST-10/17/2 REV, Paris.
   Google Scholar
• Cox, T. H., and S. Blake. 1991. Managing cultural diversity: Implications for organizational competitiveness. Academy of Management Perspectives 5 (3):45–56. doi:10.5465/ame.1991.4274465.
   Google Scholar
• Crawford, K., Dobbe R, Dryer T, Fried G, Green B, Kaziunas E, Kak A, Mathur V, McElroy E, Nill Sánchez A,et al. 2019. AI Now Report 2019. AI Now Institute, New York.
   Google Scholar
• Crawford, K., and M. Whittaker 2016. The AI now report: The social and economic implications of artificial intelligence technologies. AI Now Institute, New York.
   Google Scholar
• Curtis, C., N. Gillespie, and S. Lockey. 2022. AI-deploying organizations are key to addressing ‘perfect storm’ of AI risks. AI and Ethics 1–9. doi:10.1007/s43681-022-00163-7.
   PubMedGoogle Scholar
• Dastin, J. 2018. Amazon scraps secret AI recruiting tool that showed bias against women - reuters. Reuters :5–9. Accessed August 9, 2022. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
   Google Scholar
• Dawson, D., E. Schleiger, J. Horton, J. McLaughlin, C. Robinson, G. Quezada, J. Scowcroft, and S. Hajkowicz. 2019. Artificial intelligence: Australia’s ethics framework. Data61 CSIRO, Australia.
   Google Scholar
• DeepMind. 2019. Ethics and society principles. London: DeepMind.
   Google Scholar
• Deloitte. 2020. Trustworthy AI. USA: The Deloitte AI Institute.
   Google Scholar
• Diakopoulos, N., Friedler S, Arenas M, Barocas S, Hay M, Howe B, Jagadish HV, Unsworth K, Sahuguet A, Venkatasubramanian S,et al. 2019. Principles for accountable algorithms and a social impact statement for algorithms. Fairness, Accountability, and Transparency in Machine Learning (FAT/ML). https://www.fatml.org/resources/principles-for-accountable-algorithms
   Google Scholar
• Djabi-saïdani, A., and S. Perugien. 2019. The shaping of diversity management in France: An institutional change analysis. European Management Review 17 (1):229–46. doi:10.1111/emre.12343.
   Web of Science ®Google Scholar
• Donaldson, T. 1989. The Ethics of International Business. OxfordNew York.
   Google Scholar
• Dutch Artificial Intelligence. 2018. Dutch artificial intelligence manifesto. The Netherlands: Special Interest Group on Artificial Intelligence.
   Google Scholar
• Dwertmann, D. J. G., L. H. Nishii, and D. van Knippenberg. 2016. Disentangling the fairness and discrimination and synergy perspectives on diversity climate: Moving the field forward. Journal of Management 42 (5):1136–68. doi:10.1177/0149206316630380.
   Web of Science ®Google Scholar
• Elango, B., K. Paul, S. K. Kundu, and S. K. Paudel. 2010. Organizational ethics, individual ethics, and ethical intentions in international decision-making. Journal of Business Ethics 97:543–61.
   Web of Science ®Google Scholar
• Eubanks, V. 2018. Automating inequality: How high-tech tools profle, police, and punish the poor. New York: St. Marting’s Press.
   Google Scholar
• European Commission. 2020. The Assessment List for Trustworthy Artificial Intelligence for self assessment. In Directorate-General for Communications Networks. Content and Technology, Publications Office.
   Google Scholar
• Executive Office of the President National Science and Technology Council Committee on Technology. 2016. Report on the future of artificial intelligence, USA.
   Google Scholar
• Floridi, L., J. Cowls, M. Beltrametti, R. Chatila, P. Chazerand, V. Dignum, C. Luetge, R. Madelin, U. Pagallo, F. Rossi, et al. 2018. Ai4people—an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines. 28 (4):589–707. doi:10.1007/s11023-018-9482-5.
   Web of Science ®Google Scholar
• Freeman, R. E. 1984. Strategic management: A stakeholder theory. Journal of Management Studies 39 (1):1–21.
   Google Scholar
• Future of Life Institute. 2017. Asilomar AI principles. California: Future of Life Institute.
   Google Scholar
• The Future Society. 2018. The future society, law & society initiative, principles for the governance of AI. Policy Research, The Law & Society Initiative.
   Google Scholar
• Garcia-Gathright, J., and A. Springer 2018. Assessing and addressing algorithmic bias – but before we get there. In 2018 AAAI Spring Symposium Series, California.
   Google Scholar
• Gilligan, C. 1982. In a different voice: Psychological theory and women’s development. Cambridge: Harvard University Press.
   Google Scholar
• Google. 2018. Artificial intelligence at google: Our principles. Google AI.
   Google Scholar
• Government of the Republic of Korea. 2017. Mid- to long-term master plan in preparation for the intelligent information society: Managing the fourth industrial revolution. Korea: Government of the Republic of Korea Interdepartmental Exercise.
   Google Scholar
• GOV.UK. 2019. Initial code of conduct for data-driven health and care technology. United Kingdom: GOV.UK.
   Google Scholar
• Green Digital Working Group. 2016. Position on robotics and artificial intelligence. The Green Digital Working Group, Europe.
   Google Scholar
• Greene, D., A. L. Hoffmann, and L. Stark 2019. Better, nicer, clearer, fairer: A critical assessment of the movement for ethical artificial intelligence and machine learning. Proceedings of the 52nd Hawaii International Conference on System Sciences, Hawaii.
   Google Scholar
• Hagendorff, T. 2020. The ethics of AI ethics: An evaluation of guidelines. Minds and Machines 30 (1):99–120. doi:10.1007/s11023-020-09517-8.
   Web of Science ®Google Scholar
• Hagendorff, T. 2021. Blind spots in AI ethics. AI Ethics (4):1–17. doi:10.1007/s43681-021-00122-8.
   Google Scholar
• Hanna, A., E. Denton, A. Smart, and J. Smith-Loud 2020. Towards a critical race methodology in algorithmic fairness. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, Barcelona, Spain.
   Google Scholar
• Holstein, K., J. Vaughan, H. Daumé Iii, M. Dudik, and H. Wallach 2019. Improving fairness in machine learning systems: What do industry practitioners need? In 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK.
   Google Scholar
• House of Lords. 2018. AI in the UK: Ready, willing and able? House of Lords Select Committee on Artificial Intelligence, HL Paper 100, London.
   Google Scholar
• Howard, A., and J. Borenstein. 2018. The ugly truth about ourselves and our robot creations: the problem of bias and social inequity. Science and Engineering Ethics 24 (5):1521–36. doi:10.1007/s11948-017-9975-2.
   PubMed Web of Science ®Google Scholar
• IBM. 2018. Everyday ethics for artificial intelligence, IBM design for AI. USA.
   Google Scholar
• IEEE. 2016. Ethically aligned design: a vision for prioritizing human well-being with autonomous and intelligent systems, version 1. IEEE Advancing Technology for Humanity. https://standards.ieee.org/wp-content/uploads/import/documents/other/ead_v1.pdf
   Google Scholar
• IEEE. 2019. Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems, First Edition ed, IEEE Advancing Technology for Humanity. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9398613
   Google Scholar
• Information Accountability Foundation. 2015. Unified ethical frame for big data analysis: IAF big data ethics initiative. Part A.
   Google Scholar
• Institute of Business Ethics. 2018. Business ethics and artificial intelligence. London: Institute of Business Ethics.
   Google Scholar
• ITI. 2017. AI policy principles. Information Technology Industry Council. https://www.itic.org/public-policy/ITIAIPolicyPrinciplesFINAL.pdf
   Google Scholar
• Jain, H. C., P. J. Sloane, and F. M. Horwitz. 2003. Employment equity and affirmative action: An international comparison. New-York: ME Sharpe.
   Google Scholar
• Japanese Society for Artificial Intelligence. 2017. Ethical Guidelines. In The Japanese Society for Artificial Intelligence, Japan.
   Google Scholar
• Jobin, A., M. Ienca, and E. Vayena. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1 (9):389–99. doi:10.1038/s42256-019-0088-2.
   Google Scholar
• Kiemde, S. M. A., and A. D. Kora. 2022. Towards an ethics of AI in Africa: Rule of education. AI and Ethics 2 (1):35–40. doi:10.1007/s43681-021-00106-8.
   Google Scholar
• Klarsfeld, A., L. A. E. Booysen, E. Ng, I. Roper, and A. Tatli. 2014. Perspectives from 16 countries on diversity and equal treatment at work: An overview and transverse questions country perspectives on diversity and equal treatment. Cheltenham: Edward Elgar Publishing.
   Google Scholar
• Klarsfeld, A., L. Knappert, A. Kornau, F. W. Ngunjiri, and B. Sieben. 2019. Diversity in under-researched countries: New empirical fields challenging old theories? Equality, Diversity and Inclusion: An International Journal (7):694–704. doi:10.1108/EDI-03-2019-0110.
   Web of Science ®Google Scholar
• Kulik, C. T. 2014. Working below and above the line: The research-practice gap in diversity management. Human Resource Management Journal 24 (2):129–44. doi:10.1111/1748-8583.12038.
   Web of Science ®Google Scholar
• Landis, J., and G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics 33 (1):159–74. doi:10.2307/2529310.
   PubMed Web of Science ®Google Scholar
• Larsson, S., and F. Heintz. 2020. Transparency in artificial intelligence. Internet Policy Review 9 (2). doi:10.14763/2020.2.1469.
   Google Scholar
• Leaders of the G7. 2018. Common vision for the future of artificial intelligence, G7. Charlevoix.
   Google Scholar
• Long, D., and B. Magerko 2020. What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems, Honolulu HI USA, 1–16.
   Google Scholar
• Lorbiecki, A., and G. Jack. 2000. Critical turns in the evolution of diversity management. British Journal of Management, Special Issue, 17-31. s1. 10.1111/1467-8551.11.s1.3
   Google Scholar
• McNamara, A., J. Smith, and E. Murphy-Hill 2018. Does ACM’s code of ethics change ethical decision making in software development? In 26th ACM Joint ESE Conference and Symposium on the FSE:729–33.
   Google Scholar
• Microsoft Corporation. 2019. Microsoft AI principles. USA: Microsoft Corporation.
   Google Scholar
• Ministry of State for Science and Technology Policy. 2017. Report on Artificial Intelligence and Human Society: Unofficial Translation. In Advisory Board on Artificial Intelligence and Human Society, Japan.
   Google Scholar
• Mitchell, R. K., B. R. Agle Et, and D. J. Wood. 1997. Toward a theory of stakeholder identification and salience: Defining the principle of who and what really counts. Academy of Management Review 22 (4):85386. doi:10.2307/259247.
   Web of Science ®Google Scholar
• Mittelstadt, B. 2019. Principles alone cannot guarantee ethical AI. Nature Machine Intelligence 1 (11):501–07. doi:10.1038/s42256-019-0114-4.
   Google Scholar
• Moher, D., A. Liberati, J. Tetzlaf, D. G. Altman, and The PRISMA Group. 2009. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine 6 (7):e1000097. doi:10.1371/journal.pmed.1000097.
   PubMed Web of Science ®Google Scholar
• Montréal Declaration. 2018. Montréal declaration for a responsible development of artificial intelligence. Montreal. https://www.montrealdeclaration-responsibleai.com/
   Google Scholar
• Morley, J., L. Floridi, L. Kinsey, and A. Elhalal. 2019. A typology of ai ethics tools, methods and research to translate principles into practices. Retrieved 12 2022. from. https://aiforsocialgood.github.io/neurips2019/accepted/track2/pdfs/26_aisg_neurips2019.pdf
   Google Scholar
• Ng, E. S., and A. Klarsfeld. 2018. Comparative and multi-country research in equality, diversity and inclusion. In Handbook of research methods in diversity management, equality and inclusion at work, 122–46. Edward Elgar Publishing.
   Google Scholar
• Ng, D. T. K., J. K. L. Leung, K. W. S. Chu, and M. S. Qiao 2021. AI literacy: Definition, teaching, evaluation and ethical issues. In Proceedings of the Association for Information Science and Technology, Salt Lake City, UT, 58(1): 504–09.
   Google Scholar
• Nyholm, S., and J. Smids. 2016. The ethics of accident-algorithms for self-driving cars: An applied trolley problem?. Ethical Theory and Moral Practice 19 (5):1275–89.
   Web of Science ®Google Scholar
• Ochigame, R. (2019, December 20). The invention of ‘ethical AI’: How big tech manipulates academia to avoid regulation. The Intercept. https://theintercept.com/2019/12/20/mit-ethical-aiartificial-intelligence/
   Google Scholar
• OECD. 2019. Recommendation of the council on artificial intelligence . OECD/LEGAL/0449. https://legalinstruments.oecd.org/en/instruments/oecd-legal-0449#:~:text=The%20OECD%20Council%20adopted%20the,on%2022%2D23%20May%202019.&text=The%20OECD%20Recommendation%20on%20AI,governments%20in%20their%20implementation%20efforts.
   Google Scholar
• O’neil, C. 2016. Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
   Google Scholar
• Oswick, C., and M. Noon. 2014. Discourses of diversity, equality and inclusion: Trenchant formulations or transient fashions? British Journal of Management 25 (1):23–39. doi:10.1111/j.1467-8551.2012.00830.x.
   Web of Science ®Google Scholar
• Partnership on AI. 2018. Advancing positive outcomes for people and society.
   Google Scholar
• PwC. 2019. The responsible AI framework. PwC.
   Google Scholar
• Royal Society. 2017. Machine learning: The power and promise of computers that learn by example. London: The Royal Society.
   Google Scholar
• Sacco, J. M., and N. Schmitt. 2005. A dynamic multilevel model of demographic diversity and misfit effects. The Journal of Applied Psychology 90 (2):203–31. doi:10.1037/0021-9010.90.2.203.
   PubMed Web of Science ®Google Scholar
• Schiff, D., J. Biddle, J. Borenstein, and K. Laas 2020. What’s Next for AI Ethics, Policy, and Governance? A Global Overview. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, AIES ’20, 153–58. NewYork, NY, USA: Association for Computing Machinery.
   Google Scholar
• Selbst, A. D., D. Boyd, S. A. Friedler, S. Venkatasubramanian, and J. Vertesi 2019. Fairness and abstraction in sociotechnical systems. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, Barcelona, Spain.
   Google Scholar
• Selwyn, N., and B. Gallo Cordoba. 2021. Australian public understandings of artificial intelligence. AI & Society 37 (4):1645–62. doi:10.1007/s00146-021-01268-z.
   Web of Science ®Google Scholar
• Singh, B., D. E. Winkel, and T. T. Selvarajan. 2013. Managing diversity at work: Does psychological safety hold the key to racial differences in employee performance? Journal of Occupational & Organizational Psychology 86 (2):242–63. doi:10.1111/joop.12015.
   Web of Science ®Google Scholar
• Snyder, H. 2019. Literature review as a research methodology: An overview and guidelines. Journal of Business Research 104:333–39. doi:10.1016/j.jbusres.2019.07.039.
   Web of Science ®Google Scholar
• Syed, J., and M. Özbilgin. 2009. A relational framework for international transfer of diversity management practices. The International Journal of Human Resource Management 20 (12):2435–53. doi:10.1080/09585190903363755.
   Web of Science ®Google Scholar
• Thomas, D. A., and R. J. Ely. 1996. Making differences matter: A new paradigm for diversity management. Harvard Business Review 74 (5):79–90.
   Web of Science ®Google Scholar
• Toronto declaration. 2018. The toronto declaration, protecting the right to equality and non-discrimination in machine learning systems. Toronto: Human Rights Watch.
   Google Scholar
• Tranfield, D., D. Denyer, and P. Smart. 2003. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management 14 (3):207–22. doi:10.1111/1467-8551.00375.
   Web of Science ®Google Scholar
• UNESCO. 2021. Recommendation on the ethics of artificial intelligence. Paris: SHS/BIO/REC-AIETHICS/2021.
   Google Scholar
• UNI Global Union. 2017. Top 10 Principles for Ethical AI. Switzerland: UNI Global Union.
   Google Scholar
• Van den Bergh, J., and D. Deschoolmeester. 2010. Ethical decision making in ICT: Discussing the impact of an ethical code of conduct. Communications of the IBIMA 1–10. doi:10.5171/2010.127497.
   Google Scholar
• van Est, R., and J. Gerritsen. 2017. Human rights in the robot age: Challenges arising from the use of robotics, AI, and virtual and augmented reality. The Netherlands: Rathenau Instituut.
   Google Scholar
• Villani, C. 2018. For a meaningful artificial intelligence: Towards a French and European strategy. France: AI for Humanity.
   Google Scholar
• Voss, G. 2021. AI act: The European union’s proposed framework regulation for artificial intelligence governance. Journal of Internet Law 25 (4):1, 8–17. ( 2021).
   Google Scholar
• Wallach, W., and G. E. Marchant 2018. An agile ethical/legal model for the international and national governance of AI and robotics. Aies Conference on Artificial Intelligence, Ethics and Society, New Orleans, USA.
   Google Scholar
• Weinberg, L. 2022. Rethinking fairness: An interdisciplinary survey of critiques of hegemonic ML fairness approaches. The Journal of Artificial Intelligence Research 74:75–109. doi:10.1613/jair.1.13196.
   Web of Science ®Google Scholar
• West, S. M., M. Whittaker, and K. Crawford. 2019. Discriminating systems: Gender, race and power in AI. AI Now Institute.
   Google Scholar
• Whittaker, M., Crawford K, Dobbe R, Fried G, Kaziunas E, Mathur V, West SM, Richardson R, Schultz J, Schwartz O et al. 2018. AI now report 2018. New York: AI Now Institute.
   Google Scholar
• World Economic Forum. 2018. How to Prevent Discriminatory Outcomes in Machine Learning. In World Economic Forum Global Future Council on Human Rights 2016-18, REF, 120318–case 00040065 . Switzerland.
   Google Scholar
• Zhao, J., T. Wang, M. Yatskar, V. Ordonez, and K. W. Chang. 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. ArXiv: 170709457.
   Google Scholar
• Zou, J., and L. Schiebinger. 2018. Design AI so that it’s fair. Nature 559 (7714):324–26. doi:10.1038/d41586-018-05707-8.
   PubMed Web of Science ®Google Scholar
• Zuboff, S. 2019. The age of surveillance capitalism: The fight for a human future at the new frontier of power. New York: Public Affairs.
   Google Scholar