From predictive policing to automated credit scoring, algorithms applied on a massive scale, gone unchecked, represent a serious threat to our society. Dr. Rumman Chowdhury, director of Machine Learning Ethics, Transparency and Accountability at Twitter, joins Azeem Azhar to explore how businesses can practice responsible AI to minimize unintended bias and the risk of harm. HBR Presents is a network of podcasts curated by HBR editors, bringing you the best business ideas from the leading minds in management. The views and opinions expressed are solely those of the authors and do not necessarily reflect the official policy or position of Harvard Business Review or its affiliates.

A key component to make sure that we develop responsible AI is diversity. This is because an AI application reflects and even amplifies the biases of its developers. As I discussed in my previous post, a diverse team will see things from many different points of view and help to reflect many different perspectives in the data. What can we do besides making sure our teams are diverse? Without claiming to have the complete answer, I would like to share some thoughts.