CTO
OB
Maya Cratsley
U. of Southern California - Marshall School of Business, United States
Yiran Wang
U. of Southern California, United States
Maya Cratsley
U. of Southern California - Marshall School of Business, United States
Brian Lattimore
-, United States
Merrick Osborne
Haas School of Business, UC Berkeley, United States
Yiran Wang
U. of Southern California, United States
The workplace of the future is evolving to become not only increasingly digitized but also more diverse in its makeup. A report from McKinsey estimated that in 2020 organizations collectively spent $7.5 billion investing in diversity, equity, and inclusion efforts (DEI), predicting that number to more than double to $15.4 billion by 2026 (McKinsey & Company, 2023). At the same time, Deloitte’s 2023 Global Technology Leadership Study estimated that the average organization dedicates 5.49% of revenue to investing in technology (Deloitte Insights, 2023). These numbers point to the dual importance of diversity and technology in the modern organization. However, little work by organizational scholars considering the dual impact of increasing diversity and technological advancement in organizations, and how organizations can best prepare for this future. In order to innovate for a better future, we must consider technological innovation in tandem with goals such as increasing diversity, making organizations more equitable, and implementing fairness as a default. The four presentations in this symposium offer multiple perspectives on the key issues facing organizations of the future when it comes to diversity, equity, and technology, as well as offering potential tools and solutions that organizations can leverage as they try to adapt to the future of work. The first presentation explores how the introduction of AI-based large language models such as ChatGPT may change the landscape of organizations in a way that meaningfully impacts intergroup interactions. The second presentation also explores how AI may change organizations, examining the differential consequences of AI for members of different racial groups. The third presentation zooms out a bit, offering a novel theoretical model of how bias emerges in Machine Learning Models, and what organizations can do to prevent it. Finally, the fourth presentation looks towards the future with empirically-tested solutions for how we might better understand one another’s experiences as our organizations continue to diversify.
Author: Maya J. Cratsley – U. of Southern California - Marshall School of Business
Author: Nathanael Fast – U. of Southern California
Author: Nick Switanek – Microsoft
Author: Brian Robert Lattimore – -
Author: Merrick Osborne – Haas School of Business, UC Berkeley
Author: Maya J. Cratsley – U. of Southern California - Marshall School of Business
Author: MaQueba Massey – Iowa State U.
Author: Vincent Rice – SUNY At Buffalo
Author: Ali Omrani – U. of Southern California
Author: Yiran Wang – U. of Southern California
Author: Sarah S M Townsend – U. of Southern California