Bringing Inclusive Diversity to Data Science: Opportunities and Challenges

Abstract

As data science research continues to expand into a variety of applied fields, the need for talented and diverse individuals has been widely acknowledged. Despite this acknowledgement, data science lags behind other STEM disciplines in achieving a diverse workforce. Through work we have undertaken in the past as part of the Broadening Participation in Data Mining workshop (BPDM) and our work with ACM SIGKDD, we seek to build a better workforce that is positioned to address the data science problems of the next hundred years. A significant barrier to trainee long-term career success is their limited ability of underrepresented trainees to demonstrate their analytical abilities and sophisticated inferential talents to address key data issues in our community. In this talk we will present an overview of the goals of the Diversity and Inclusion track and share our vision for how we bridge this diversity divide that our society and our data science workforce needs right now. We are interested in how diversity is encountered across ethnic, gender, and ability identities. To this end we have prepared an exciting new program activities to facilitate broader conversations in the data science field that cover not only technical ideas but innovative thinking in what the future of data science can look like if we diversify the group of contributors and enlarge those included.