Papers

Learn more about AI2's Lasting Impact Award
Viewing 1-10 of 11 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 BuchananFAccT2022 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…
  • Gender trends in computer science authorship

    Lucy Lu Wang, Gabriel Stanovsky, Luca Weihs, Oren EtzioniCACM2021 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 EtzioniCACM2020 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 OrdonezICCV2019 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. SmithACL2019 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…
  • Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets

    Mor Geva, Yoav Goldberg, Jonathan BerantarXiv2019 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…
  • Evaluating Gender Bias in Machine Translation

    Gabriel Stanovsky, Noah A. Smith, Luke ZettlemoyerACL2019 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…
  • Quantifying Sex Bias in Clinical Studies at Scale With Automated Data Extraction

    Sergey Feldman, Waleed Ammar, Kyle Lo, Elly Trepman, Madeleine van Zuylen, Oren EtzioniJAMA2019 Importance: Analyses of female representation in clinical studies have been limited in scope and scale. Objective: To perform a large-scale analysis of global enrollment sex bias in clinical studies. Design, Setting, and Participants: In this cross…
  • Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

    Hila Gonen, Yoav GoldbergNAACL2019 Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious…
  • Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

    Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordóñez, Kai-Wei ChangEMNLP2017 Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and…