World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
15569
World Ranking
7758
National Ranking
3352

Overview

Hanna Wallach is affiliated with Microsoft in the United States. Their research primarily concentrates in the field of Computer Science with a focus on Artificial Intelligence and Safety Research, alongside work in Cognitive Neuroscience, Management Information Systems, and Computer Vision and Pattern Recognition.

Wallach's scholarly output includes publications in notable venues such as arXiv (Cornell University), Proceedings of the ACM on Human-Computer Interaction, Communications of the ACM, the 2022 ACM Conference on Fairness, Accountability, and Transparency, and SSRN Electronic Journal. These venues reflect a strong engagement with both foundational and applied aspects of computer science research.

Their recent papers demonstrate a focus on ethical and social issues within AI, machine learning fairness, data documentation, and representational harms in technology. Key publications include:

  • "Datasheets for datasets," 2021, Communications of the ACM
  • "Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support," 2022, Proceedings of the ACM on Human-Computer Interaction
  • "Toward fairness in AI for people with disabilities SBG@a research roadmap," 2020, ACM SIGACCESS Accessibility and Computing
  • "Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata," 2022, Proceedings of the ACM on Human-Computer Interaction
  • "Measuring Representational Harms in Image Captioning," 2022, 2022 ACM Conference on Fairness, Accountability, and Transparency

Wallach's work covers several main topics, including:

  • Ethics and Social Impacts of AI
  • Explainable Artificial Intelligence (XAI)
  • Machine Learning and Data Classification
  • Big Data and Business Intelligence
  • Adversarial Robustness in Machine Learning
  • Psychology of Moral and Emotional Judgment
  • Data Stream Mining Techniques

They have collaborated frequently with several researchers, contributing to a network of scholarly partnerships that include Jennifer Wortman Vaughan, Solon Barocas, Alexandra Chouldechova, Angelina Wang, and Su Lin Blodgett.

Overall, Hanna Wallach's research engages deeply with the intersections of technology and society, focusing on fairness, transparency, and the social implications of AI systems.

Best Publications

  • Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

    Unknown

  • Optimizing Semantic Coherence in Topic Models

    David Mimno;Hanna Wallach;Edmund Talley;Miriam Leenders

  • Topic modeling: beyond bag-of-words

    Hanna M. Wallach

  • Datasheets for datasets

    Timnit Gebru;Jamie Morgenstern;Briana Vecchione;Jennifer Wortman Vaughan

  • Evaluation methods for topic models

    Hanna M. Wallach;Iain Murray;Ruslan Salakhutdinov;David Mimno

  • Rethinking LDA: Why Priors Matter

    Hanna M. Wallach;David M. Mimno;Andrew McCallum

  • Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?

    Kenneth Holstein;Jennifer Wortman Vaughan;Hal Daumé;Miro Dudik

  • Language (Technology) is Power: A Critical Survey of "Bias" in NLP

    Su Lin Blodgett;Solon Barocas;Hal Daumé;Hanna M. Wallach

  • Manipulating and Measuring Model Interpretability

    Forough Poursabzi-Sangdeh;Daniel G Goldstein;Jake M Hofman;Jennifer Wortman Wortman Vaughan

  • Datasheets for Datasets

    Timnit Gebru;Jamie Morgenstern;Briana Vecchione;Jennifer Wortman Vaughan

  • Conditional Random Fields: An Introduction

    Hanna M Wallach

  • Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

    Harmanpreet Kaur;Harsha Nori;Samuel Jenkins;Rich Caruana

  • A Reductions Approach to Fair Classification

    Alekh Agarwal;Alina Beygelzimer;Miroslav Dudík;John Langford

  • Understanding the Effect of Accuracy on Trust in Machine Learning Models

    Ming Yin;Jennifer Wortman Vaughan;Hanna Wallach

  • Polylingual Topic Models

    David Mimno;Hanna M. Wallach;Jason Naradowsky;David A. Smith

  • Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

    Michael A. Madaio;Luke Stark;Jennifer Wortman Vaughan;Hanna Wallach

  • Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology

    Ran Zmigrod;Sabrina J. Mielke;Hanna M. Wallach;Ryan Cotterell

  • Manipulating and Measuring Model Interpretability

    Forough Poursabzi-Sangdeh;Daniel G. Goldstein;Jake M. Hofman;Jennifer Wortman Vaughan

  • Efficient Training of Conditional Random Fields

    Hanna Wallach

  • Structured Topic Models for Language

    Hanna M. Wallach

  • Fairlearn: A toolkit for assessing and improving fairness in AI

    Sarah Bird;Miro Dudík;Richard Edgar;Brandon Horn

  • Generating summary keywords for emails using topics

    Mark Dredze;Hanna M. Wallach;Danny Puller;Fernando Pereira

  • A Reductions Approach to Fair Classification

    Alekh Agarwal;Alina Beygelzimer;Miroslav Dudík;John Langford

  • Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets

    Su Lin Blodgett;Gilsinia Lopez;Alexandra Olteanu;Robert Sim

  • Database of NIH grants using machine-learned categories and graphical clustering

    Edmund M Talley;David Newman;David Mimno;David Mimno;Bruce W Herr

Frequent Co-Authors

Hal Daumé
Hal Daumé University of Maryland, College Park
Ryan Cotterell
Ryan Cotterell ETH Zurich
Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
David Mimno
David Mimno Cornell University
Mingyuan Zhou
Mingyuan Zhou The University of Texas at Austin
David M. Blei
David M. Blei Columbia University
Fernando Diaz
Fernando Diaz Microsoft (United States)
Kate Crawford
Kate Crawford Microsoft (United States)
Meredith Ringel Morris
Meredith Ringel Morris Google (United States)
Ece Kamar
Ece Kamar Microsoft (United States)

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