Impact: We developed new toolkits and methods to help AI developers

To achieve more responsible artificial intelligence (AI) systems, we need to support the industry practitioners building them. 

Our research has directly informed the development of new practical methods and software toolkits for responsible AI. Fairness toolkits support and empower developers to build more equitable and responsible AI systems by making fairness a central part of the development lifecycle. These toolkits and methods are used by AI developers worldwide.

We conducted the first studies to understand what challenges AI developers face when attempting to develop responsible AI systems, resulting in new methods and tools that are more useful in practice. 

The findings of this work led to...

  • The development of Fairlearn, an open-source, community-driven project to help data scientists improve fairness of AI systems. Fairlearn currently has about 100 contributors, spanning multiple technology companies and universities across the world. This research also informed the design of responsible AI toolkits, guidelines, and playbooks, such as FairCompass, ABOUT ML, The Situate AI Guidebook and The Generative AI Ethics Playbook.
  • Improve the usability and usefulness of existing responsible AI toolkits, such as IBM’s AI Fairness 360 toolkit.
  • The development of several new algorithmic methods that can assess and mitigate unfairness in AI systems even when individual demographic data is unavailable. This is a common situation in many real-world settings where organizations may be prohibited from collecting or using certain demographic data.
  • New software toolkits and platforms for responsible AI development, including Zeno, WeAudit, and Policy Projector.
  • Through our team’s discussions with companies such as OpenAI, Microsoft, Google and UL, this research informed approaches to user engagement in AI red teaming, testing, and auditing in industry.
  • Inform reports, such as the National Telecommunications and Information Administration (NTIA)’s report on “Artificial intelligence accountability policy.”

Supported by:  The National Science Foundation (NSF), Microsoft Research, Apple, PwC, Amazon, IBM, K&L Gates, and the CMU Block Center for Technology and Society

Timing:  This line of research started in 2018 and is ongoing.

Related work:

Researchers:  Jeff Bigham, Alex Cabrera, Sauvik Das, Wesley Deng, Motahhare Eslami, Jodi Forlizzi, Hoda Heidari, Ken Holstein, Jason Hong, Ji-Youn Jung, Anna Kawakami, Mary Beth Kery, Hank Lee, Michael Madaio, Dominik Moritz, Adam Perer, Hong Shen, Zhiwei Steven Wu, Nur Yildirim, Haiyi Zhu, John Zimmerman

Research Areas:  Human-Centered AI, Tools and Programming, Responsible Computing, Ethics and Policy

 

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