January 28th, 2022
Title: Digital Advertising, Privacy, and Competition
Bio: Robin Berjon is an expert in Web technology with almost two decades’ worth of experience in both Web development and driving standardization efforts, notably within W3C. He is in charge of data governance at The New York Times. He has a fondness for ranting and rambling, especially in writing, but generally starts to feel self-conscious after writing a few sentences about himself in the third person.
February 4th, 2022
Bio: Margaret Hu is a Professor of Law and of International Affairs, Co-Hire for the Institute for Computational and Data Sciences, and Faculty Member of the Institute for Network and Security Research in the College of Engineering at The Pennsylvania State University. She also serves as Penn State Law's inaugural Dean for Non-JD Programs. Her research interests include the intersection of immigration policy, national security, cybersurveillance, and civil rights. She has published several works on dataveillance and cybersurveillance, including, Biometric ID Cybersurveillance; Big Data Blacklisting; Taxonomy of the Snowden Disclosures; Biometric Cyberintelligence and the Posse Comitatus Act; and Algorithmic Jim Crow. She is currently a member of the Advisory Board of the Future of Privacy Forum, a non-profit think tank in Washington, D.C., that promotes responsible data privacy policies. Previously, she served as special policy counsel in the Office of Special Counsel for Immigration-Related Unfair Employment Practices (OSC), Civil Rights Division, U.S. Department of Justice. Dean Hu holds a B.A. from the University of Kansas and a J.D. from Duke Law School. She clerked for Judge Rosemary Barkett on U.S. Court of Appeals for the Eleventh Circuit, and subsequently joined the U.S. Department of Justice through the Attorney General’s Honors Program.
February 11th, 2022
Bio: Maarten Sap is a Postdoc/Young Investigator at the Allen Institute for AI (AI2), working on project Mosaic, and will be starting as an assistant professor at CMU's LTI department. His research focuses on endowing NLP systems with social intelligence and social commonsense, and understanding social inequality and bias in language. He received my PhD from the University of Washington where he was advised by Noah Smith and Yejin Choi, and have interned at AI2 working on social commonsense reasoning, and at Microsoft Research working on deep learning models for understanding human cognition.
February 25th, 2022
Bio: Angela Stewart is a Postdoctoral Fellow in the Human-Computer Interaction Institute at Carnegie Mellon University, working under Dr. Amy Ogan. She graduated with her PhD in Computer Science from the University of Colorado Boulder, advised by Dr. Sidney D'Mello. Her work sits at the intersection of education, human-computer interaction, and artificial intelligence. She create socio-technical interventions for more equitable and inclusive educational spaces.
March 11th, 2022
Bio: Dr. Thema Monroe-White has over 10 years of combined evaluation, research and data analytics expertise from her years as a consultant, nonprofit leader, and instructor. Dr. White's research is concerned with broadening participation in STEM-C fields, with a special emphasis on information technology, analytics and data science. She is particularly interested in the relationship between technology, entrepreneurship and the economic empowerment of underrepresented people of color. She presents regularly at professional and academic conferences and publishes in a wide variety of journals pertaining to equity and inclusion in STEM education, social entrepreneurship and innovation. Dr. White holds a PhD in Science, Technology and Innovation Policy from the Georgia Institute of Technology as well as Master’s and Bachelor’s degrees from Howard University.
March 25th, 2022
Bio: Brooke Foucault Welles is an associate professor in the Department of Communication Studies and a core faculty member of the Network Science Institute at Northeastern University. Combining the methods of computational social science and network science with the theories of communication studies, Foucault Welles studies how online communication networks enable and constrain behavior, with particular emphasis on how these networks enable the pursuit of individual, team, and collective goals.
April 1st, 2022
Bio: Dr. Aisha Walcott-Bryant is a research scientist and manager at IBM Research Africa - Nairobi, Kenya. She leads a team of researchers and engineers that use AI, Blockchain, and other technologies to develop innovations in Water Access and Management, core AI, and Healthcare, particularly for emerging countries. She has a strong interest in global health and development. Her team's recent healthcare work on Enabling Care Continuity was awarded honorable mention in the International Conference on Health Informatics, ICHI2019. When Aisha joined the IBM Research Africa lab, she led the research efforts in mobility and transportation for developing cities. The aim was to provide significant and impactful value-added services that ease the movement of people, goods, and services in Africa. She and her team developed innovative intelligent transportation systems data capture methods and analytical tools, to provide computational understanding about the local driving and infrastructure context. Prior to her career at IBM Research, Aisha worked in Spain in the area of Smarter Cities at Barcelona Digital and Telefonica. She earned her PhD in the Electrical Engineering and Computer Science Department at MIT in robotics, as a member of the Computer Science and Artificial Intelligent lab (CSAIL).
April 8th, 2022
Bio: Mine Çetinkaya-Rundel is a Professor of the Practice and the Director of Undergraduate Studies at the Department of Statistical Science and an affiliated faculty in the Computational Media, Arts, and Cultures program at Duke University. Her work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education. I work on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. In addition to her academic position, she also works with RStudio, where she focuses primarily on education for open-source R packages as well as building resources and tools for educators teaching statistics and data science with R and RStudio.
April 15th, 2022
Bio: Katie Shilton is an associate professor in the College of Information Studies at the University of Maryland, College Park, and leads the Ethics & Values in Design (EViD) Lab. Her research explores ethics and policy for the design of information technologies. She is the PI of the PERVADE project, a multi-campus collaboration focused on big data research ethics. Other projects include developing privacy-sensitive search for email collections; analyzing ethical cultures in computer security research; and building tools to facilitate ethics discussions in mobile application development.
April 22nd, 2022
Bio: Byron Wallace is an assistant professor in the Khoury College of Computer Sciences at Northeastern University. He earned his PhD from Tufts University in 2012, after which he taught at Brown University as research faculty. He joined Northeastern from the University of Texas at Austin, where he was an assistant professor in the School of Information from 2014-2016. Wallace’s research areas include artificial intelligence, data science, machine learning, natural language processing, and information retrieval, with emphasis on applications in health informatics. Byron is a member of the applied machine learning group and the Data Science and Analytics Lab at Northeastern. Wallace develops machine learning and natural language processing methods that make synthesizing the vast biomedical evidence-base more efficient. He also works on core machine learning and natural language processing methods, with his more of his recent work concerning Convolutional Neural Network (CNN) architectures for text. Wallace has recently been developing hybrid, interactive human/machine learning systems that aim to robustly combine human and machine intelligence.
December 3rd, 2021
Title: Explanation through Argumentation
Bio: Tjitze Rienstra is an assistant professor at the Department of Data Science & Knowledge Engineering, Faculty of Science and Engineering, Maastricht University, The Netherlands. His research focuses on Explainable AI, computational models of argumentation, and reasoning under uncertainty.
November 19th, 2021
Title: Diversity and Inequality in Social Networks
Bio: Ana-Andreea Stoica is a Ph.D. candidate at Columbia University. Her work focuses on mathematical models, data analysis, and inequality in social networks. From recommendation algorithms to the way information spreads in networks, Ana is particularly interested in studying the effect of algorithms on people's sense of privacy, community, and access to information and opportunities. Since 2019, she has been co-organizing the Mechanism Design for Social Good initiative.
- Stoica, A.-A., Han, J. X., & Chaintreau, A. (2020). Seeding Network Influence in Biased Networks and the Benefits of Diversity. In Proceedings of The Web Conference 2020, WWW ’20. ACM. Retrieved from https://dl.acm.org/doi/10.1145/3366423.3380275
- Stoica, A.-A., Riederer, C., & Chaintreau, A. (2018). Algorithmic Glass Ceiling in Social Networks. In Proceedings of the 2018 World Wide Web Conference, WWW ’18. ACM. Retrieved from https://doi.org/10.1145/3178876.3186140
- Finocchiaro, J., Maio, R., Monachou, F., Patro, G. K., Raghavan, M., Stoica, A.-A., & Tsirtsis, S. (2021). Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’21. ACM. Retrieved from https://doi.org/10.1145/3442188.3445912
November 12th, 2021
Title: Partial Perspective and Situated Knowledge: A Feminist Appraisal of Machine Learning & AI
Bio: Laura K. Nelson is an assistant professor of sociology at the University of British Columbia. She uses computational methods – principally text analysis, natural language processing, machine learning, and network analysis techniques – to study social movements, culture, gender, and organizations and institutions. Previously, she was an assistant professor of sociology at Northeastern University, a postdoctoral research fellow at Northwestern University, and a postdoctoral fellow at the Data Science Institute and Digital Humanities at the University of California, Berkeley, which is also where she received her PhD. She has published in outlets such as the American Journal of Sociology, Gender & Society, Poetics, Mobilization: An International Quarterly, and Sociological Methods & Research. She is currently on the editorial board of Sociological Methodology and is an associate editor at EPJ Data Science.
- Nelson, L. K. (2021). Leveraging the alignment between machine learning and intersectionality: Using word embeddings to measure intersectional experiences of the nineteenth century U.S. South. Poetics (p. 101539). Elsevier BV. Retrieved from https://doi.org/10.1016/j.poetic.2021.101539; Replication Repository
- Nelson, L. K. (2017). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research 49(1), 3 - 42. SAGE Publications. Retrieved from https://doi.org/10.1177/0049124117729703; Replication Repository
- Nelson, L. K. (2019). To Measure Meaning in Big Data, Don’t Give Me a Map, Give Me Transparency and Reproducibility. Sociological Methodology, 49(1), 139 – 143. SAGE Publications. https://doi.org/10.1177/0081175019863783
November 5th, 2021
Title: Quantifying the Role of Display Advertising in the Disinformation Ecosystem
Bio: Ceren Budak is an Assistant Professor of Information at the School of Information at the University of Michigan. Her research interests lie in the area of computational social science. She utilizes network science, machine learning, and crowdsourcing methods and draws from scientific knowledge across multiple social science communities to contribute computational methods to the field of political communication.
- Bozarth, L., & Budak, C. (2021). Market Forces: Quantifying the Role of Top Credible Ad Servers in the Fake News Ecosystem. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 83-94. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18043/
- Bozarth, L., & Budak, C. (2021). An Analysis of the Partnership between Retailers and Low-credibility News Publishers. Journal of Quantitative Description: Digital Media, 1. Retrieved from https://doi.org/10.51685/jqd.2021.010
- Bozarth, L., Saraf, A., & Budak, C. (2020). Higher Ground? How Groundtruth Labeling Impacts Our Understanding of Fake News about the 2016 U.S. Presidential Nominees. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 48-59. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/7278
October 29th, 2021
Title: Teaching Responsible Data Science
Bio: Julia Stoyanovich is an Institute Associate Professor of Computer Science & Engineering at the Tandon School of Engineering, Associate Professor of Data Science at the Center for Data Science, and Director of the Center for Responsible AI at New York University (NYU). Her research focuses on responsible data management and analysis: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data science lifecycle. She established the "Data, Responsibly" consortium and served on the New York City Automated Decision Systems Task Force, by appointment from Mayor de Blasio. Julia developed and has been teaching courses on Responsible Data Science at NYU, and is a co-creator of an award-winning comic book series on this topic. In addition to data ethics, Julia works on the management and analysis of preference and voting data, and on querying large evolving graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics & Statistics from the University of Massachusetts at Amherst. She is a recipient of an NSF CAREER award and a Senior Member of the ACM.
- Falaah Arif Khan and Julia Stoyanovich. “Mirror, Mirror”. Data, Responsibly Comics, Volume 1 (2020). Retrieved from https://dataresponsibly.github.io/comics/vol1/mirror_en.pdf
- Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich. “Fairness and Friends”. Data, Responsibly Comics, Volume 2 (2021). Retrieved from https://dataresponsibly.github.io/comics/vol2/fairness_en.pdf
- Stoyanovich, J., Howe, B., & Jagadish, H. V. (2020). Responsible data management. In Proceedings of the VLDB Endowment (Vol. 13, Issue 12, pp. 3474–3488). Retrieved from https://doi.org/10.14778/3415478.3415570
October 22nd, 2021
Title: Acknowledging potential pitfalls in social media research – between researchers practices and structured documentation approaches
Bio: Dr. Katrin Weller is leading the Digital Society Observatory team as part of GESIS’ Computational Social Science department. From 2021-2023 she is also co-leading the Research Data & Methods unit at the Center for Advanced Internet Studies (CAIS). In her work she looks into how researchers across disciplines use data from Web and Social Media Platforms as new types of research data – and how this leads to new challenges along the research process.
- Sen, I., Flöck, F., Weller, K., Weiß, B., & Wagner, C. (2021). A Total Error Framework for Digital Traces of Human Behavior on Online Platforms. Public Opinion Quarterly. Volume 85, Issue S1, 2021, Pages 399–422. Retrieved from https://doi.org/10.1093/poq/nfab018
- Kinder-Kurlanda, K.E., & Weller, K. (2020). Perspective: Acknowledging Data Work in the Social Media Research Lifecycle. Frontiers in Big Data 3:509954. doi: 10.3389/fdata.2020.509954. Retrieved from https://www.frontiersin.org/articles/10.3389/fdata.2020.509954/full
- Weller, K., & Kinder-Kurlanda, K.E. (2015). Uncovering the Challenges in Collection, Sharing and Documentation: The Hidden Data of Social Media Research? In Standards and Practices in Large-Scale Social Media Research: Papers from the 2015 ICWSM Workshop. Proceedings Ninth International AAAI Conference on Web and Social Media Oxford University, May 26, 2015 – May 29, 2015, 28-37. Ann Arbor, MI: AAAI Press. Retrieved from AAAI
October 15th, 2021
Title: Generating Post-hoc Explanations for ML Models Using Contrastive Counterfactuals
Bio: Babak Salimi is an assistant professor in HDSI at UC San Diego. Before joining UC San Diego, he was a postdoctoral research associate in the Department of Computer Science and Engineering, University of Washington, where he worked with Prof. Dan Suciu and the database group. He received his Ph.D. from the School of Computer Science at Carleton University, advised by Prof. Leopoldo Bertossi. His research seeks to unify techniques from theoretical data management, causal inference and machine learning to develop a new generation of decision-support systems that help people with heterogeneous background to interpret data. His ongoing work in causal relational learning aims to develop the necessary conceptual foundations to make causal inference from complex relational data. Further, his research in the area of responsible data science develops needed foundations for ensuring fairness and accountability in the era of data-driven decisions. His research contributions have been recognized with a Research Highlight Award in ACM SIGMOD, a Best Demonstration Paper Award at VLDB and a Best Paper Award in ACM SIGMOD.
October 8th, 2021
Title: Challenges to the Foresight and Measurement of Computational Harms Time
Bio: Alexandra Olteanu is a principal researcher at Microsoft Research Montréal, part of the Fairness, Accountability, Transparency, and Ethics (FATE) group. Her work currently examines practices and assumptions made when evaluating a range of computational systems, particularly measurements aimed at quantifying possible computational harms. Before joining Microsoft Research, Alexandra was a Social Good Fellow at IBM's T.J. Watson Research Center. Her work has been featured in governmental reports and in popular media outlets. Alexandra has co-organized tutorials/workshops and has served on the program committee of all major web and social media conferences, including SIGIR, ICWSM, KDD, WSDM, WWW, as the Tutorial Co-chair for ICWSM 2018, 2020 and FAccT 2018. She also sits on the steering committee of the ACM Conference on Fairness, Accountability, and Transparency. Alexandra holds a Ph.D. in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
- Robertson, R. E., Olteanu, A., Diaz, F., Shokouhi, M., & Bailey, P. (2021, May 6). “I Can’t Reply with That”: Characterizing Problematic Email Reply Suggestions. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. CHI ’21: CHI Conference on Human Factors in Computing Systems. doi: 10.1145/3411764.3445557. Retrieved from Microsoft Research
- Olteanu, A., Diaz, F., & Kazai, G. (2020). When Are Search Completion Suggestions Problematic? Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1–25. doi: 10.1145/3415242. Retrieved from Microsoft Research
- Boyarskaya, M., Olteanu, A., and Crawford, K. (2020). Overcoming Failures of Imagination in AI Infused System Development and Deployment. Paper presented at the Navigating the Broader of Impacts of AI Research (NeurIPS 2020) Workshop. Retrieved from arXiv.org
October 1st, 2021
Title: Privacy preferences and choice architecture: the case of consent management on the web
Bio: Rainer Böhme is professor of Computer Science and head of the Security & Privacy Lab at the University of Innsbruck in the Austrian Alps. His background is interdisciplinary with degrees in Communication Science, Economics, and Computer Science. A large part of his research concerns the design or evaluation of technical systems with impact on society at large. This includes privacy, forensics, cyber risk, and most recently digital money. See https://informationsecurity.uibk.ac.at/people/rainer-boehme/ for more information.
- Hils, M., Woods, D. W., & Böhme, R. (2021). Privacy Preference Signals: Past, Present and Future. Proceedings on Privacy Enhancing Technologies, 2021(4), 249–269. Retrieved from arXiv.rog
- Hils, M., Woods, D.W., and Böhme, R. (2020). Measuring the Emergence of Consent Management on the Web. In Proceedings of the ACM Internet Measurement Conference (IMC). Retrieved from https://dl.acm.org/doi/10.1145/3419394.3423647
- Woods, D.W. and Böhme, R. (2020). The Commodification of Consent. In Workshop on the Economics of Information Security (WEIS). Brussels, Belgium. Retrieved from WEIS2020
- Machuletz, D., & Böhme, R. (2020). Multiple Purposes, Multiple Problems: A User Study of Consent Dialogs after GDPR. Proceedings on Privacy Enhancing Technologies, 2020(2), 481–498. Retrieved from arXiv.rog
- Böhme, R., & Köpsell, S. (2010). Trained to accept? Proceedings of the 28th International Conference on Human Factors in Computing Systems - CHI ’10. the 28th international conference, Atlanta, Georgia, pp. 2403–2406. Retrieved from https://dl.acm.org/doi/pdf/10.1145/1753326.1753689
September 24th, 2021
Title: Designing an Informative and Usable Security and Privacy Label for IoT Devices
Bio: Pardis Emami-Naeini (https://homes.cs.washington.edu/~pemamina/) is a postdoctoral scholar in the Security and Privacy Research Lab at the University of Washington. Her research is broadly at the intersection of security and privacy, usability, and human-computer interaction. Her work has been published at top venues in security (IEEE S&P, SOUPS) and human-computer interaction and social sciences (CHI, CSCW) and covered by multiple outlets, including Wired and the Wall Street Journal. Pardis received her B.Sc. degree in computer engineering from Sharif University of Technology in 2015 and the M.Sc. and Ph.D. degrees in computer science from Carnegie Mellon University in 2018 and 2020, respectively. She was selected as a Rising Star in electrical engineering and computer science in October 2019 and was awarded the 2019-2020 CMU CyLab Presidential Fellowship.
- Pardis Emami-Naeini, Janarth Dheenadhayalan, Lorrie Cranor, and Yuvraj Agarwal. (2021). Which Privacy and Security Attributes Most Impact Consumers’ Risk Perception and Willingness to Purchase IoT Devices?. In Proceedings of the 42nd IEEE Symposium on Security and Privacy (S&P ’21). Retrieved from https://homes.cs.washington.edu/~pemamina/publications/sp21/sp21-paper.pdf
- Pardis Emami-Naeini, Yuvraj Agarwal, Lorrie Cranor, and Hanan Hibshi. (2020). Ask the Experts: What Should Be on an IoT Privacy and Security Label?. In Proceedings of the 41st IEEE Symposium on Security and Privacy (S&P ’20). Retrieved from https://homes.cs.washington.edu/~pemamina/publications/sp20/sp20-paper.pdf
- Pardis Emami-Naeini, Yuvraj Agarwal, Lorrie Cranor, and Henry Dixon. (2019). Exploring How Privacy and Security Factor into IoT Device Purchase Behavior. In Proceedings of the 37th ACM Conference on Human Factors in Computing Systems (CHI ’19). Retrieved from https://homes.cs.washington.edu/~pemamina/publications/chi19/chi19-paper.pdf
Semptember 17th, 2021
Title: Amplifying the Griot: (Ancient) Stories Guiding the Design of Fair, Equitable & Transparent Systems
Bio: Lindah Kotut is an assistant professor in the Information School at the University of Washington. She completed her Ph.D. in computer science from Virginia Tech where she was advised by Dr. Scott McCrickard. Her research is at the intersection of Human-Computer Interaction (HCI) and Indigenous Knowledge (IK)
- Kotut, L., Bhatti, N., Saaty, M., Haqq, D., Stelter, T. L., & McCrickard, D. S. (2020). Clash of Times: Respectful Technology Space for Integrating Community Stories in Intangible Exhibits. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Retrieved from https://doi.org/10.1145/3313831.3376354
September 10th, 2021
Title: Algorithmic Bias in Education: From Unknown Bias to Known Bias to Fairness to Equity
Bio: Ryan Baker is an Associate Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics. His lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but which predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments, and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 150 researchers in 7 countries. Predictive analytics models he helped develop have been used to benefit over a million students, over a hundred thousand people have taken MOOCs he ran, and he has coordinated longitudinal studies that spanned over a decade. He was the founding president of the International Educational Data Mining Society, is currently serving as Editor of the journal Computer-Based Learning in Context, is Associate Editor of the Journal of Educational Data Mining, was the first technical director of the Pittsburgh Science of Learning Center DataShop, and currently serves as Co-Director of the MOOC Replication Framework (MORF). Baker has co-authored published papers with over 400 colleagues.
- Baker, R. S., & Hawn, A. (2021). Algorithmic Bias in Education. Retrieved from https://doi.org/10.35542/osf.io/pbmvz.
- Paquette, L., Ocumpaugh, J., Li, Z., Andres, J.M.A.L., Baker, R.S. (2020) Who's Learning? Using Demographics in EDM Research. Journal of Educational Data Mining, 12(3), 1-30. Retrieved from https://doi.org/10.5281/zenodo.4143612.
September 3rd, 2021
Title: Faculty hiring, social class, and epistemic inequality
Bio: Allison Morgan is currently a data scientist at Twitter. Broadly, she’s interested in using causal inference and network science, joining surveys with big data, and studying fairness and social inequality by building systems. Her research has measured the structural factors that drive a lack of diversity in science and highlighted their consequences. She earned her PhD in Computer Science at University of Colorado, Boulder, where she was supported by the NSF GRFP. Her research has been published in PNAS and Science Advances, and covered by outlets such as the Washington Post and Scientific American.
- Morgan, A.C., Economou, D.J., Way, S.F. et al. (2018). Prestige drives epistemic inequality in the diffusion of scientific ideas. EPJ Data Sci. 7, 40. Retrieved from https://doi.org/10.1140/epjds/s13688-018-0166-4.
- Morgan, A., Clauset, A., Larremore, D., LaBerge, N., & Galesic, M. (2021, March 24). Socioeconomic Roots of Academic Faculty. Retrieved from https://doi.org/10.31235/osf.io/6wjxc.
August 27th, 2021
Title: Towards Building Equitable Language Technologies
Bio: Su Lin Blodgett is a postdoctoral researcher in the Fairness, Accountability, Transparency, and Ethics (FATE) group at Microsoft Research Montréal. She is broadly interested in examining the social implications of natural language processing technologies. Her work currently focuses on better conceptualizing and measuring harms arising from language technologies, and on uncovering practices, assumptions, and constraints surrounding the production of these technologies. Previously, she completed her Ph.D. in computer science at the University of Massachusetts Amherst.
- Blodgett, Su Lin, et al. (2020). Language (Technology) is Power: A Critical Survey of “Bias” in NLP. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
- Blodgett, Su Lin, et al. (2021). Stereotyping Norwegian salmon: an inventory of pitfalls in fairness benchmark datasets. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics.