Diversity and Inclusion in AI: Lessons from Human and AI Collaboration

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Explore the operationalization of diversity and inclusion in AI requirements and the role of GPT-4 in enhancing this process.

Authors: Muneera Bano; Didar Zowghi; Vincenzo Gervasi; Rifat Shams. Table of Links Abstract, Impact Statement, and Introduction Defining Diversity and Inclusion in AI Research Motivation Research Methodology Results Discussion Conclusion and Future Work and References VII.CONCLUSION AND FUTURE WORK In this paper we have presented our extensive research on exploring D&I in AI guidelines and our attempt to operationalise them in the process of specifying D&I in AI requirements.

Roche, C., P. Wall, and D. Lewis, Ethics and diversity in artificial intelligence policies, strategies and initiatives. AI and Ethics, 2022: p. 1-21. Hickok, M., Lessons learned from AI ethics principles for future actions. AI and Ethics, 2021. 1: p. 41-47. Dattner, B., et al., The legal and ethical implications of using AI in hiring. Harvard Business Review, 2019. 25. Hagendorff, T., The ethics of AI ethics: An evaluation of guidelines. Minds and machines, 2020. 30: p. 99-120. Leavy, S., B.

Roche, C., P. Wall, and D. Lewis, Ethics and diversity in artificial intelligence policies, strategies and initiatives. AI and Ethics, 2022: p. 1-21. Hickok, M., Lessons learned from AI ethics principles for future actions. AI and Ethics, 2021. 1: p. 41-47. Dattner, B., et al., The legal and ethical implications of using AI in hiring. Harvard Business Review, 2019. 25. Hagendorff, T., The ethics of AI ethics: An evaluation of guidelines. Minds and machines, 2020. 30: p. 99-120. Leavy, S., B.

 

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