Skip down to main content

AI & Data Diversity

AI & Data Diversity


Data now drive new types of business models in an increasing number of sectors of the economy, and the rapid expansion of jobs in data science and artificial intelligence presents new opportunities for many people. But data science and artificial intelligence have an enormous implementation problem. How do the results of analysis get made into socially viable choices, decisions and actions? As the data revolution extends to more organizations and sectors, the social impact of data and the effectiveness of data analytics teams requires processes and policies at the ground level. The everyday choices and decisions made by individuals and groups determines how data are used, which has implications as well as economic growth opportunities for women and public life. The AI & Data Diversity project explores three main areas of research:

  1. Diversity in Data Science. It is known that team diversity is for good decision‐making. How does diversity in gender, ethnicity, and knowledge discipline change the process of the translation and synthesis of data science into good and equitable deliberations, designs, and decisions?
  2. Data Science in Practice. The “practice” of data‐driven decision‐making varies greatly from the “science”. How do communication strategies, human biases, and organizational routines shape the translation and synthesis of data into action? Are there predictable patterns in the use and misuse of data in everyday practice?
  3. Social Impact of Data. It has been the case that individual data scientists often articulate ethical concerns. But what social and organizational configurations need to be in place to help people surface and implement ethical values and choices with large‐scale data?

Scientific research can advance both public understanding of data diversity and everyday decisions around AI and technology innovation broadly. The AI & Data Diversity project is uniquely positioned to advance public debate and understanding of how data diversity improves social equality. Ultimately, the broader impacts of this work will help build better technologies and strengthen the science on diversity in technology‐led economic growth.

Key Information

  • Microsoft
  • Project dates:
    August 2019 - January 2021

    Related Topics