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

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3 Jul 2024

Authors:

(1) Muneera Bano;

(2) Didar Zowghi;

(3) Vincenzo Gervasi;

(4) Rifat Shams.

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. We have identified 23 unique themes related to D&I in AI considerations from our literature review. We introduced a user story template for articulating D&I in AI requirements, and we have conducted a focus group with four human analysts to develop user stories for two cases of AI systems to gain insights into the process of writing D&I in AI requirements. Furthermore, we have explored the utility and usefulness of using GPT-4 as an agent in the automation of generating D&I in AI requirements. Comparing the user stories developed by human analysts and those generated by GPT-4 we have gained insights into the pros and cons of the use of LLM in this activity and the complementary nature of this form of human-machine collaboration. There remains a need for further exploration of the influence of cultural and legal contexts on the implementation of diversity and inclusion requirements in AI. Investigations could delve into how variances in privacy laws, data protection regulations, and cultural perspectives impact AI system development across different regions. Moreover, research could be undertaken to assess the effects of various AI systems, such as recognition technology, on individuals from diverse backgrounds encompassing race, gender, and age.

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1 st Author: Muneera Bano, PhD is Senior Research Scientist and member of Diversity and Inclusion team at CSIRO’s Data61. She is an award-winning scholar, is passionate advocate for gender equity in STEM. She is a Diversity Inclusion and Belongingness (DIB) officer at Data61 and a member of the 'Equity, Diversity and Inclusion’ committee for Science and Technology Australia. Muneera graduated with a PhD in Software Engineering from UTS in 2015. She has published more than 50 research articles in notable international forums on Software Engineering. Her research, influenced by her interest in AI and Diversity and Inclusion, emphasizes humancentric technologies.

Contact her at [email protected]

Official Webpage: https://people.csiro.au/B/M/muneera-bano

2nd Author: Didar Zowghi, (PhD, IEEE Member since 1995) is a Senior Principal Research Scientist and leads the science team for Diversity and Inc(lusion in AI at CSIRO’s Data61. She is an Emeritus Professor at the University of Technology Sydney (UTS) and conjoint professor at the University of New South Wales (UNSW). She has decades of experience in Requirements Engineering research and practice. In 2019 she received the IEEE Lifetime Service Award for her contributions to the RE research community, and in 2022 the Distinguished Educator Award from IEEE Computer Society TCSE. She has published over 220 research articles in prestigious conferences and journals and has co-authored papers with over 100 researchers from 30+ countries.

Contact her at [email protected]

Official Webpage: https://people.csiro.au/z/D/Didar-Zowghi

3rd Author: Vincenzo Gervasi, PhD is an associate professor in the University of Pisa’s Computer Science Department. His research focuses on natural language processing applied to requirements engineering, formal specifications, and software architectures, fields in which he has published over 140 papers in international venues. Prof. Gervasi received his PhD in computer science from the University of Pisa and is a member of IFIP WG 2.9.

Contact him at [email protected]

This paper is available on arxiv under CC 4.0 license.