AI & Fairness

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

Leaders

Oren EtzioniChief Executive Officer

Nicole DeCarioOperations

AI2 is committed to diversity, equity, and inclusion.

Read about ethical guidelines for crowdsourcing from AI2.

Recent Papers

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Gender trends in computer science authorship
Lucy Lu Wang, Gabriel Stanovsky, Luca Weihs, Oren EtzioniCACM • 2021 A comprehensive and up-to-date analysis of Computer Science literature (2.87 million papers through 2018) reveals that, if current trends continue, parity between the number of male and female authors will not be reached in this century. Under our most… more
Green AI
Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren EtzioniCACM • 2020 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… more
Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, Vicente OrdonezICCV • 2019 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… more
The Risk of Racial Bias in Hate Speech Detection
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, Noah A. SmithACL • 2019 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… more
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets
Mor Geva, Yoav Goldberg, Jonathan BerantarXiv • 2019 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… more