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AI & Fairness

We are building on AI2's expertise in NLP, computer vision, and engineering to deliver a tangible positive impact on fairness.

Over the next few months, we'll be working with renowned researchers and experts to continue shaping this project. Join us!

Leaders

  • Dr. Oren Etzioni is Chief Executive Officer at AI2. He has been a Professor at the University of Washington's Computer Science department since 1991. His awards include Seattle's Geek of the Year (2013), and he has founded or co-founded several companies, including Farecast and Decide. He has written over 100 technical papers, as well as commentary on AI for The New York Times and Nature. He helped to pioneer meta-search, online comparison shopping, machine reading, and Open Information Extraction. Learn more Learn more

    Oren Etzioni

  • Nicole loves solving problems, particularly ones that result in better outcomes for people and communities. Prior to joining AI2, Nicole spent the bulk of her career in the philanthropy and non-profit sector working on social justice issues. Nicole received her Bachelor's degree in Music from the University of Miami and her Master's degree in Public Administration from the University of Washington. A New Jersey native, she now calls Seattle home and loves traveling and exploring with her family.

    Nicole DeCario

We are hiring! Please see our current openings.

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AI2 is committed to diversity, equity, and inclusion.

Read about ethical guidelines for crowdsourcing from AI2.

Research

Recent AI2 publications related to AI & Fairness.

  • Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations ICCV • 2019 Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, Vicente Ordonez
    In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables --such as gender-- in visual recognition tasks. We show that trained models significantly amplify the association of target labels with gender beyond what one would expect from biased…  (More)
  • The Risk of Racial Bias in Hate Speech Detection ACL • 2019 Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, Noah A. Smith
    We investigate how annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. We first uncover unexpected correlations between surface markers of African American English (AAE) and…  (More)
  • Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets arXiv • 2019 Mor Geva, Yoav Goldberg, Jonathan Berant
    Crowdsourcing has been the prevalent paradigm for creating natural language understanding datasets in recent years. A common crowdsourcing practice is to recruit a small number of high-quality workers, and have them massively generate examples. Having only a few workers generate the majority of…  (More)
  • Evaluating Gender Bias in Machine Translation ACL • 2019 Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer
    We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., "The doctor asked the…  (More)
  • Green AI arXiv • 2019 Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren Etzioni
    The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint [38]. Ironically, deep learning was inspired by the human brain, which is…  (More)
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“By working arm-in-arm with multiple stakeholders, we can address the important topics rising at the intersection of AI, people, and society.”
— Eric Horvitz