FATE: Fairness, Accountability, Transparency, and Ethics in AI

FATE: Fairness, Accountability, Transparency, and Ethics in AI




As we harness the power of AI, machine learning, and data science throughout many aspects of society and Microsoft systems and products, we need to consider the larger issues with AI.

These techniques raise complex ethical and social questions: How can we best use AI to assist users and offer enhanced insights, while avoiding exposing them to discrimination in health, housing, law enforcement, and employment? How can we balance the need for efficiency and exploration with fairness and sensitivity to users? As we move toward relying on intelligent agents in our everyday lives, how do we ensure that individuals and communities can trust these systems?

As researchers in this group, we work on the complex social implications of AI, machine learning, data science, large-scale experimentation, and increasing automation. Our aim is to facilitate computational techniques that are both innovative and ethical, while drawing on the deeper context surrounding these issues from sociology, history, and science and technology studies. We are working on collaborative research projects that address the need for transparency, accountability, and fairness in AI and machine learning systems. We publish in a variety of disciplines, including machine learning, information retrieval, systems, sociology, political science, and science and technology studies.



Machine Learning and Fairness Webinar

In this webinar led by Microsoft researchers Jenn Wortman Vaughan and Hanna Wallach, 15-year veterans of the machine learning field, you’ll learn how to make detecting and mitigating biases a first-order priority in your development and deployment of ML systems.

Topics include:

  • the main types of harm that can arise;
  • the subpopulations most likely to be affected;
  • the origins of these harms and strategies for mitigating them;
  • and some recently developed software tools to help
Watch on demand

Download the presentation deck (PDF)