AI & Fairness

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

Leaders

Nicole DeCarioOperations

AI2 is committed to diversity, equity, and inclusion.

Read about ethical guidelines for crowdsourcing from AI2.

Recent Papers

View All AI & Fairness Papers

Measuring the Carbon Intensity of AI in Cloud Instances
Jesse Dodge, Taylor Prewitt, Rémi Tachet des Combes, Erika Odmark, Roy Schwartz, Emma Strubell, A. Luccioni, Noah A. Smith, Nicole DeCario, Will BuchananFAccT • 2022 The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a…
Related:
Video
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…
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…
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…
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…