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

Oren EtzioniChief Executive Officer

Nicole DeCarioOperations

We are hiring! Please see our current openings.

AI2 is committed to diversity, equity, and inclusion.

Read about ethical guidelines for crowdsourcing from AI2.

Recent Papers

View All AI & Fairness Papers

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 • 2019In 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
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, Noah A. SmithACL • 2019We 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
Mor Geva, Yoav Goldberg, Jonathan BerantarXiv • 2019Crowdsourcing 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
Gabriel Stanovsky, Noah A. Smith, Luke ZettlemoyerACL • 2019We 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
Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren EtzioniarXiv • 2019The 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