publications
Preprints
Measuring Machine Learning Harms from Stereotypes: Requires Understanding Who is Being Harmed by Which Errors in What Ways. Angelina Wang, Xuechunzi Bai, Solon Barocas, and Su Lin Blodgett. [paper]
Evaluating the Social Impact of Generative AI Systems in Systems and Society. Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan K. Baker, Su Lin Blodgett, Hal Daumé III, Jesse Dodge, Ellie Evans, Sara Hooker, Yacine Jernite, Alexandra Sasha Luccioni, Alberto Lusoli, Margaret Mitchell, Jessica Newman, Marie-Therese Png, Andrew Strait, and Apostol Vassilev. [paper]
Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities. Samer B. Nashed, Justin Svegliato, and Su Lin Blodgett. [paper]
Risks of AI Foundation Models in Education. Su Lin Blodgett and Michael Madaio. [paper]
How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLP. Christian Hardmeier, Marta R. Costa-jussà, Kellie Webster, Will Radford, and Su Lin Blodgett. [paper]
2024
ECBD: Evidence-Centered Benchmark Design for NLP. Yu Lu Liu, Su Lin Blodgett, Jackie Chi Kit Cheung, Q. Vera Liao, Alexandra Olteanu, and Ziang Xiao. ACL. [paper]
Understanding the Impacts of Language Technologies’ Performance Disparities on African American Language Speakers. Jay L. Cunningham, Su Lin Blodgett, Hal Daumé III, Christina Harrington, Hanna Wallach, and Michael Madaio. Findings of ACL. [paper]
“One-size-fits-all?” Examining Expectations around What Constitute “Fair” or “Good” NLG System Behaviors. Li Lucy, Su Lin Blodgett, Milad Shokouhi, Hanna Wallach, and Alexandra Olteanu. NAACL. [paper]
The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels. Eve Fleisig, Su Lin Blodgett, Dan Klein, and Zeerak Talat. NAACL. [paper]
2023
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices. Yu Lu Liu, Meng Cao, Su Lin Blodgett, Jackie Chi Kit Cheung, Alexandra Olteanu, and Adam Trischler. Findings of EMNLP. [paper]
FairPrism: Evaluating Fairness-Related Harms in Text Generation. Eve Fleisig, Aubrie N. Amstutz, Chad Atalla, Su Lin Blodgett, Hal Daumé III, Alexandra Olteanu, Emily Sheng, Dan Vann, and Hanna Wallach. ACL. [paper]
It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance. Arjun Subramonian, Xingdi Yuan, Hal Daumé III, and Su Lin Blodgett. Findings of ACL. [paper]
This Prompt is Measuring <MASK>: Evaluating Bias Evaluation in Language Models. Seraphina Goldfarb-Tarrant, Eddie L. Ungless, Esma Balkır, and Su Lin Blodgett. Findings of ACL. [paper]
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging. Jared Katzman, Angelina Wang, Morgan Scheuerman, Su Lin Blodgett, Kristen Laird, Hanna Wallach, and Solon Barocas. AAAI. [paper]
2022
Examining Responsibility and Deliberation in AI Impact Statements and Ethics Reviews. David Liu, Priyanka Nanayakkara, Sarah Sakha, Grace Abuhamad, Su Lin Blodgett, Nicholas Diakopoulos, Jessica Hullman and Tina Eliassi-Rad. AIES. [paper]
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications. Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, and Alexandra Olteanu. NAACL. [paper]
Beyond “Fairness”: Structural Injustice Lenses On AI for Education. Michael Madaio, Su Lin Blodgett, Elijah Mayfield, and Ezekiel Dixon-Román. Invited chapter in The Ethics of Artificial Intelligence in Education: Current Challenges, Practices and Debates, Wayne Holmes and Kaśka Porayska-Pomsta (Eds.), Routledge. Forthcoming.
Examining Political Rhetoric with Epistemic Stance Detection. Ankita Gupta, Su Lin Blodgett, Justin Gross, and Brendan O’Connor. Workshop on Natural Language Processing and Computational Social Science (NLP+CSS). [paper]
2021
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets. Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna Wallach. ACL. [paper] [slides]
A Survey of Race, Racism, and Anti-Racism in NLP. Anjalie Field, Su Lin Blodgett, Zeerak Talat, and Yulia Tsvetkov. ACL. [paper]
2020
PhD Thesis: Sociolinguistically Driven Approaches for Just Natural Language Processing. Su Lin Blodgett. [thesis]
Language (Technology) is Power: A Critical Survey of “Bias” in NLP. Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. ACL. [paper] [slides]
2018
Twitter Universal Dependency Parsing for African-American and Mainstream American English. Su Lin Blodgett, Johnny Tian-Zheng Wei, and Brendan O’Connor.ACL. [paper] [data]
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses. Katherine Keith, Su Lin Blodgett, and Brendan O’Connor. NAACL. [paper]
2017
A Dataset and Classifier for Recognizing Social Media English. Su Lin Blodgett, Johnny Tian-Zheng Wei, and Brendan O’Connor. Workshop on Noisy User-Generated Text (W-NUT). W-NUT Best Paper Award. [paper] [data]
Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English. Su Lin Blodgett and Brendan O’Connor. Fairness, Accountability, and Transparency in Machine Learning Workshop (FAT/ML). [paper] [data]
2016
Demographic Dialectal Variation in Social Media: A Case Study of African-American English. Su Lin Blodgett, Lisa Green, and Brendan O’Connor. EMNLP. [paper] [data]
Visualizing Textual Models with In-Text and Word-as-Pixel Highlighting. Abram Handler, Su Lin Blodgett, and Brendan O’Connor. Proceedings of the Workshop on Human Interpretability in Machine Learning (WHI). [paper]