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ORCIDSu Lin Blodgett
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2020 – today
- 2024
[c20]Jay Cunningham, Su Lin Blodgett, Michael Madaio, Hal Daumé III, Christina Harrington, Hanna M. Wallach:
Understanding the Impacts of Language Technologies' Performance Disparities on African American Language Speakers. ACL (Findings) 2024: 12826-12833- [c19]Yu Lu Liu, Su Lin Blodgett, Jackie C. K. Cheung, Vera Liao, Alexandra Olteanu, Ziang Xiao:
ECBD: Evidence-Centered Benchmark Design for NLP. ACL (1) 2024: 16349-16365 - [c18]Li Lucy, Su Lin Blodgett, Milad Shokouhi, Hanna M. Wallach, Alexandra Olteanu:
"One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviors. NAACL-HLT 2024: 1054-1089 - [c17]Eve Fleisig, Su Lin Blodgett, Dan Klein, Zeerak Talat:
The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels. NAACL-HLT 2024: 2279-2292 - [i21]Angelina Wang, Xuechunzi Bai, Solon Barocas, Su Lin Blodgett:
Measuring machine learning harms from stereotypes: requires understanding who is being harmed by which errors in what ways. CoRR abs/2402.04420 (2024) - [i20]Eve Fleisig, Su Lin Blodgett, Dan Klein, Zeerak Talat:
The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels. CoRR abs/2405.05860 (2024) - [i19]Yu Lu Liu, Su Lin Blodgett, Jackie Chi Kit Cheung, Q. Vera Liao, Alexandra Olteanu, Ziang Xiao:
ECBD: Evidence-Centered Benchmark Design for NLP. CoRR abs/2406.08723 (2024) - 2023
- [c16]Jared Katzman, Angelina Wang, Morgan Klaus Scheuerman, Su Lin Blodgett, Kristen Laird, Hanna M. Wallach, Solon Barocas:
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging. AAAI 2023: 14277-14285 - [c15]Seraphina Goldfarb-Tarrant, Eddie Ungless, Esma Balkir, Su Lin Blodgett:
This prompt is measuring \textlessmask\textgreater: evaluating bias evaluation in language models. ACL (Findings) 2023: 2209-2225 - [c14]Arjun Subramonian, Xingdi Yuan, Hal Daumé III, Su Lin Blodgett:
It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance. ACL (Findings) 2023: 3234-3279 - [c13]Eve Fleisig, Aubrie Amstutz, Chad Atalla, Su Lin Blodgett, Hal Daumé III, Alexandra Olteanu, Emily Sheng, Dan Vann, Hanna M. Wallach:
FairPrism: Evaluating Fairness-Related Harms in Text Generation. ACL (1) 2023: 6231-6251 - [c12]Yu Lu Liu, Meng Cao, Su Lin Blodgett, Jackie Chi Kit Cheung, Alexandra Olteanu, Adam Trischler:
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices. EMNLP (Findings) 2023: 6246-6261 - [i18]Samer B. Nashed, Justin Svegliato, Su Lin Blodgett:
Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities. CoRR abs/2301.05753 (2023) - [i17]Jared Katzman, Angelina Wang, Morgan Klaus Scheuerman, Su Lin Blodgett, Kristen Laird, Hanna M. Wallach, Solon Barocas:
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging. CoRR abs/2305.01776 (2023) - [i16]Arjun Subramonian, Xingdi Yuan, Hal Daumé III, Su Lin Blodgett:
It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance. CoRR abs/2305.09022 (2023) - [i15]Seraphina Goldfarb-Tarrant, Eddie Ungless, Esma Balkir, Su Lin Blodgett:
This Prompt is Measuring : Evaluating Bias Evaluation in Language Models. CoRR abs/2305.12757 (2023) - [i14]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, Apostol Vassilev:
Evaluating the Social Impact of Generative AI Systems in Systems and Society. CoRR abs/2306.05949 (2023) - [i13]Li Lucy, Su Lin Blodgett, Milad Shokouhi, Hanna M. Wallach, Alexandra Olteanu:
"One-size-fits-all"? Observations and Expectations of NLG Systems Across Identity-Related Language Features. CoRR abs/2310.15398 (2023) - [i12]Yu Lu Liu, Meng Cao, Su Lin Blodgett, Jackie Chi Kit Cheung, Alexandra Olteanu, Adam Trischler:
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices. CoRR abs/2311.11103 (2023) - 2022
- [c11]David Liu, Priyanka Nanayakkara, Sarah Ariyan Sakha, Grace Abuhamad, Su Lin Blodgett, Nicholas Diakopoulos, Jessica R. Hullman, Tina Eliassi-Rad:
Examining Responsibility and Deliberation in AI Impact Statements and Ethics Reviews. AIES 2022: 424-435 - [c10]Su Lin Blodgett, Q. Vera Liao, Alexandra Olteanu, Rada Mihalcea, Michael J. Muller, Morgan Klaus Scheuerman, Chenhao Tan, Qian Yang:
Responsible Language Technologies: Foreseeing and Mitigating Harms. CHI Extended Abstracts 2022: 152:1-152:3 - [c9]Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, Alexandra Olteanu:
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications. NAACL-HLT 2022: 314-324 - [i11]Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, Alexandra Olteanu:
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications. CoRR abs/2205.06828 (2022) - [i10]Ankita Gupta, Su Lin Blodgett, Justin H. Gross, Brendan T. O'Connor:
Examining Political Rhetoric with Epistemic Stance Detection. CoRR abs/2212.14486 (2022) - 2021
- [c8]Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, Hanna M. Wallach:
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets. ACL/IJCNLP (1) 2021: 1004-1015 - [c7]Anjalie Field
, Su Lin Blodgett, Zeerak Waseem, Yulia Tsvetkov:
A Survey of Race, Racism, and Anti-Racism in NLP. ACL/IJCNLP (1) 2021: 1905-1925 - [i9]Christian Hardmeier, Marta R. Costa-jussà, Kellie Webster, Will Radford, Su Lin Blodgett:
How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLP. CoRR abs/2104.03026 (2021) - [i8]Michael Madaio, Su Lin Blodgett, Elijah Mayfield, Ezekiel Dixon-Román:
Confronting Structural Inequities in AI for Education. CoRR abs/2105.08847 (2021) - [i7]Anjalie Field, Su Lin Blodgett, Zeerak Waseem, Yulia Tsvetkov:
A Survey of Race, Racism, and Anti-Racism in NLP. CoRR abs/2106.11410 (2021) - [i6]Su Lin Blodgett, Michael Madaio:
Risks of AI Foundation Models in Education. CoRR abs/2110.10024 (2021) - 2020
- [c6]Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach:
Language (Technology) is Power: A Critical Survey of "Bias" in NLP. ACL 2020: 5454-5476 - [c5]Abigail Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach:
The meaning and measurement of bias: lessons from natural language processing. FAT* 2020: 706 - [i5]Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach:
Language (Technology) is Power: A Critical Survey of "Bias" in NLP. CoRR abs/2005.14050 (2020)
2010 – 2019
- 2018
- [c4]Su Lin Blodgett, Johnny Wei, Brendan T. O'Connor:
Twitter Universal Dependency Parsing for African-American and Mainstream American English. ACL (1) 2018: 1415-1425 - [c3]Katherine A. Keith, Su Lin Blodgett, Brendan T. O'Connor:
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses. NAACL-HLT 2018: 917-928 - [i4]Katherine A. Keith, Su Lin Blodgett, Brendan O'Connor:
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses. CoRR abs/1804.06004 (2018) - 2017
- [c2]Su Lin Blodgett, Johnny Wei, Brendan O'Connor:
A Dataset and Classifier for Recognizing Social Media English. NUT@EMNLP 2017: 56-61 - [i3]Su Lin Blodgett, Brendan O'Connor:
Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English. CoRR abs/1707.00061 (2017) - 2016
- [c1]Su Lin Blodgett, Lisa Green, Brendan T. O'Connor:
Demographic Dialectal Variation in Social Media: A Case Study of African-American English. EMNLP 2016: 1119-1130 - [i2]Abram Handler, Su Lin Blodgett, Brendan O'Connor:
Visualizing textual models with in-text and word-as-pixel highlighting. CoRR abs/1606.06352 (2016) - [i1]Su Lin Blodgett, Lisa Green, Brendan O'Connor:
Demographic Dialectal Variation in Social Media: A Case Study of African-American English. CoRR abs/1608.08868 (2016)
Coauthor Index
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last updated on 2024-09-26 01:55 CEST by the dblp team
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