azjacobs at umich.edu
CV | Google Scholar
Assistant Professor of Information, School of Information
Assistant Professor of Complex Systems, College of Literature, Science, and the Arts
University of Michigan
CV | Google Scholar
ABIGAIL Z. JACOBS
Assistant Professor of Information, School of Information
Assistant Professor of Complex Systems, College of Literature, Science, and the Arts
University of Michigan
I am a computational social scientist and an Assistant Professor of Information at the University of Michigan in the School of Information and an Assistant Professor of Complex Systems in the College of Literature, Science, and the Arts. I am also an affiliate of the Center for Ethics, Society, and Computing (ESC) and the Michigan Institute for Data Science (MIDAS).
My current research interests are around structure, governance, and inequality in sociotechnical systems; measurement; and social networks. Find my academic work below, on my CV, or on Google Scholar.
Since 2019, I am an Assistant Professor at the University of Michigan School of Information and the Center for the Study of Complex Systems. Previously I was a postdoctoral fellow at the Haas School of Business at UC Berkeley and a member of the Algorithmic Fairness and Opacity Working Group. I received a PhD in Computer Science from the University of Colorado Boulder. During my PhD I was fortunate to spend time at Microsoft Research NYC (intern/consulting researcher, 2015-2017) and to have funding from an NSF Graduate Research Fellowship. In 2015, I served as an organizer for the Women in Machine Learning Workshop, a technical workshop co-located with NIPS, and from 2018-2019 I was on the Board of Directors for Women in Machine Learning, Inc. I previously received a BA in Mathematical Methods in the Social Sciences and Mathematics from Northwestern University.
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My current research interests are around structure, governance, and inequality in sociotechnical systems; measurement; and social networks. Find my academic work below, on my CV, or on Google Scholar.
Since 2019, I am an Assistant Professor at the University of Michigan School of Information and the Center for the Study of Complex Systems. Previously I was a postdoctoral fellow at the Haas School of Business at UC Berkeley and a member of the Algorithmic Fairness and Opacity Working Group. I received a PhD in Computer Science from the University of Colorado Boulder. During my PhD I was fortunate to spend time at Microsoft Research NYC (intern/consulting researcher, 2015-2017) and to have funding from an NSF Graduate Research Fellowship. In 2015, I served as an organizer for the Women in Machine Learning Workshop, a technical workshop co-located with NIPS, and from 2018-2019 I was on the Board of Directors for Women in Machine Learning, Inc. I previously received a BA in Mathematical Methods in the Social Sciences and Mathematics from Northwestern University.
Selected talks & conferences
Privacy Law Scholars Conference, June 2023 Joint work with Amina Abdu, Lauren Chambers, Deirdre K. Mulligan
University of Washington Center for an Informed Public. May 2, 2023
Yale Law School, Information Society Project, Data (Re)Makes the World. March 2023. Joint work with Amina Abdu, Lauren Chambers, Deirdre K. Mulligan
Carnegie Mellon University, Statistics seminar. October 2022
Stanford Graduate School of Business, Organizational Behavior seminar. October 2022
Data & Society, The Social Life of Algorithmic Harms workshop, 2022.
Joint work with Andrea Thomer and Jeff Lockhart
NeurIPS Workshop on AI for Science: Mind the Gaps. [short paper: Scientific Argument with Supervised Learning with Jeff Lockhart] December 2021
KDD Workshop on Measures and Best Practices for Responsible AI. [short paper: Measurement as governance in and for responsible AI] August 2021
Networks 2021 [paper: A large-scale comparative study of informal social networks in firms] July 2021
Networked Justice symposium at Networks 2021. [talk: Measurement as governance] June 2021
Statistical Inference for Network Models symposium at Networks 2021. Organizer. June 23, 2021
Machine learning and economic inequality conference. Invited speaker. [video: Measurement as governance, slides] April 2021
ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2021 [paper: Measurement and Fairness] March 2021
NIST Workshop on Bias in AI. Panelist. August 2020
Michigan Institute for Data Science Workshop on Learning environments in the time of COVID-19: (Towards) Evidence-Driven Innovation and Resilience at the University of Michigan. Organizer. June 2020
WebConf Workshop on Innovative Ideas in Data Science. Nominated for best paper. [short paper: Internet-Human Infrastructures: Lessons from Havana’s StreetNet] April 2020
ACM Conference on Fairness, Accountability, and Transparency (FAccT) Translation Tutorial: The Meaning and Measurement of Bias: Lessons from NLP. with Su Lin Blodgett, Solon Barocas, Hal Daumé III, & Hanna Wallach. [slides, relevant paper, video] January 2020
International Conference on Computational Social Science (IC2S2) July 2019
Women in Tech: The Future of AI Symposium. Panelist: Accountability in AI. November 2018
International Conference on Computational Social Science (IC2S2) July 2018
International Conference on Web and Social Media (ICWSM) Beyond Online Data workshop. Nominated for best paper award. June 2018
Algorithmic Fairness & Opacity Summer Workshop Panelist: Bias in Algorithms [Summary] June 2018
Selected ongoing projects
The Hidden Governance of AI. The Regulatory Review (2022). [link]
A. Z. Jacobs, Deirdre Mulligan.
Notre Dame-IBM Tech Ethics Lab grant. (2022-2023) “Expanding AI Audits To Include Instruments: Accountability, Measurements, and Data in Motion Capturing Technology.” Co-PI with Mona Sloane (New York University), Emanuel Moss (Intel Research).
Scientific argument with supervised learning. NeurIPS Workshop on AI for Science: Mind the Gaps. [working draft]
Jeff Lockhart, A. Z. Jacobs.
Measurement as governance in and for responsible AI. KDD Workshop on Responsible AI (2021) [working draft]
A. Z. Jacobs
Publications
The Role of Relevance in Fair Ranking.
Forthcoming in ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023. [pdf]
Aparna Balagopalan, A. Z. Jacobs, Asia J. Biega
An empirical analysis of racial categories in the algorithmic fairness literature.
Forthcoming in ACM Conference on Fairness, Accountability and Transparency (FAccT), 2023
Amina Abdu, Irene V. Pasquetto, A. Z. Jacobs
Conceptualizing Algorithmic Stigmatization.
ACM Conference on Human Factors in Computing Systems (CHI), 2023. [pdf]
Nazanin Andalibi, Cassidy Pyle, Kristen Barta, Lu Xian, A. Z. Jacobs, Mark Ackerman.
A large-scale comparative study of informal social networks in firms.
Management Science. (2021). [publisher link, open access pdf]
A. Z. Jacobs, Duncan Watts.
Measurement and fairness.
ACM Conference on Fairness, Accountability and Transparency (FAccT). (2021). [arXiv, related tutorial, slides, tutorial video]
A. Z. Jacobs, Hanna Wallach.
Internet-human infrastructures: Lessons from Havana’s StreetNet.
WebConf Workshop on Innovative Ideas in Data Science, nominated for best paper. (2020). [preprint]
A. Z. Jacobs, Michaelanne Dye Thomas (joint work).
Translation tutorial: The meaning and measurement of bias: Lessons from natural language processing
ACM Conference on Fairness, Accountability and Transparency (FAccT). (2020). [ACM link, related tutorial, slides, tutorial video]
A. Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna Wallach.
Assembly in populations of social networks.
CSCW Workshop on Multi-Site Research. Short paper. (2018). [arXiv]
A. Z. Jacobs.
Assembling thefacebook: Using heterogeneity to understand online social network assembly. ACM Web Science Conference (WebSci). (2015). [arXiv, data]
A. Z. Jacobs, Samuel F. Way, Johan Ugander & Aaron Clauset.
Learning latent block structure in weighted networks. Journal of Complex Networks 3(2), 221-248. (2015). [arXiv]
Christopher Aicher, A. Z. Jacobs & Aaron Clauset.
A unified view of generative models for networks: models, methods, opportunities, and challenges. NIPS Workshop on Networks: From Graphs to Rich Data. (2014). [arXiv]
A. Z. Jacobs & Aaron Clauset.
Efficiently inferring community structure in bipartite networks. Physical Review E 90, 012805. (2014). [arXiv, code]
Daniel B. Larremore, Aaron Clauset & A. Z. Jacobs.
Complex life cycles in a pond food web: effects of life stage structure and parasites on network properties, trophic positions and the fit of a probabilistic niche model. Oecologia 174 (3) 953-965. (2014). [publisher link]
Daniel L. Preston, A. Z. Jacobs, Sarah A. Orlofske & Pieter T.J. Johnson.
Adapting the stochastic block model to edge-weighted networks. ICML Workshop on Structured Learning (SLG). (2013). [arXiv, code]
Christopher Aicher, A. Z. Jacobs & Aaron Clauset.
Detecting friendship within dynamic online interaction networks. AAAI Conference on Weblogs and Social Media (ICWSM). (2013). [arXiv]
Sears Merritt, A. Z. Jacobs, Winter Mason & Aaron Clauset.
See also:
Comparative, population-level analysis of social networks in organizations. Dissertation. 2017.
Untangling the roles of parasites in food webs with generative network models. Preprint. (2015). [arXiv]
A. Z. Jacobs, Jennifer A. Dunne, Cristopher Moore & Aaron Clauset.
Adapting to non-stationarity with growing expert ensembles. Preprint. (2011). [arXiv]
Cosma R. Shalizi, A. Z. Jacobs, Kristina L. Klinkner & Aaron Clauset.
The Hidden Governance of AI. The Regulatory Review (2022). [link]
A. Z. Jacobs, Deirdre Mulligan.
Notre Dame-IBM Tech Ethics Lab grant. (2022-2023) “Expanding AI Audits To Include Instruments: Accountability, Measurements, and Data in Motion Capturing Technology.” Co-PI with Mona Sloane (New York University), Emanuel Moss (Intel Research).
Scientific argument with supervised learning. NeurIPS Workshop on AI for Science: Mind the Gaps. [working draft]
Jeff Lockhart, A. Z. Jacobs.
Measurement as governance in and for responsible AI. KDD Workshop on Responsible AI (2021) [working draft]
A. Z. Jacobs
Publications
The Role of Relevance in Fair Ranking.
Forthcoming in ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023. [pdf]
Aparna Balagopalan, A. Z. Jacobs, Asia J. Biega
An empirical analysis of racial categories in the algorithmic fairness literature.
Forthcoming in ACM Conference on Fairness, Accountability and Transparency (FAccT), 2023
Amina Abdu, Irene V. Pasquetto, A. Z. Jacobs
Conceptualizing Algorithmic Stigmatization.
ACM Conference on Human Factors in Computing Systems (CHI), 2023. [pdf]
Nazanin Andalibi, Cassidy Pyle, Kristen Barta, Lu Xian, A. Z. Jacobs, Mark Ackerman.
A large-scale comparative study of informal social networks in firms.
Management Science. (2021). [publisher link, open access pdf]
A. Z. Jacobs, Duncan Watts.
Measurement and fairness.
ACM Conference on Fairness, Accountability and Transparency (FAccT). (2021). [arXiv, related tutorial, slides, tutorial video]
A. Z. Jacobs, Hanna Wallach.
Internet-human infrastructures: Lessons from Havana’s StreetNet.
WebConf Workshop on Innovative Ideas in Data Science, nominated for best paper. (2020). [preprint]
A. Z. Jacobs, Michaelanne Dye Thomas (joint work).
Translation tutorial: The meaning and measurement of bias: Lessons from natural language processing
ACM Conference on Fairness, Accountability and Transparency (FAccT). (2020). [ACM link, related tutorial, slides, tutorial video]
A. Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna Wallach.
Assembly in populations of social networks.
CSCW Workshop on Multi-Site Research. Short paper. (2018). [arXiv]
A. Z. Jacobs.
Assembling thefacebook: Using heterogeneity to understand online social network assembly. ACM Web Science Conference (WebSci). (2015). [arXiv, data]
A. Z. Jacobs, Samuel F. Way, Johan Ugander & Aaron Clauset.
Learning latent block structure in weighted networks. Journal of Complex Networks 3(2), 221-248. (2015). [arXiv]
Christopher Aicher, A. Z. Jacobs & Aaron Clauset.
A unified view of generative models for networks: models, methods, opportunities, and challenges. NIPS Workshop on Networks: From Graphs to Rich Data. (2014). [arXiv]
A. Z. Jacobs & Aaron Clauset.
Efficiently inferring community structure in bipartite networks. Physical Review E 90, 012805. (2014). [arXiv, code]
Daniel B. Larremore, Aaron Clauset & A. Z. Jacobs.
Complex life cycles in a pond food web: effects of life stage structure and parasites on network properties, trophic positions and the fit of a probabilistic niche model. Oecologia 174 (3) 953-965. (2014). [publisher link]
Daniel L. Preston, A. Z. Jacobs, Sarah A. Orlofske & Pieter T.J. Johnson.
Adapting the stochastic block model to edge-weighted networks. ICML Workshop on Structured Learning (SLG). (2013). [arXiv, code]
Christopher Aicher, A. Z. Jacobs & Aaron Clauset.
Detecting friendship within dynamic online interaction networks. AAAI Conference on Weblogs and Social Media (ICWSM). (2013). [arXiv]
Sears Merritt, A. Z. Jacobs, Winter Mason & Aaron Clauset.
See also:
Comparative, population-level analysis of social networks in organizations. Dissertation. 2017.
Untangling the roles of parasites in food webs with generative network models. Preprint. (2015). [arXiv]
A. Z. Jacobs, Jennifer A. Dunne, Cristopher Moore & Aaron Clauset.
Adapting to non-stationarity with growing expert ensembles. Preprint. (2011). [arXiv]
Cosma R. Shalizi, A. Z. Jacobs, Kristina L. Klinkner & Aaron Clauset.
Advising
Ph.D. students, UMich School of Information
Working with me:
Candidates interested in pursuing a PhD should apply and be admitted to the UMSI PhD program (deadline: Dec 1). If you are a University of Michigan student and are interested in doing research with me, you are welcome to email me and include your interests, major, degree, and resume/CV.
Potential postdocs: I am a faculty mentor through the MIDAS Data Science Fellows program
I was on the job market 2018-19. My job market materials are available here: Research, Teaching, Diversity statement
Working with me:
Candidates interested in pursuing a PhD should apply and be admitted to the UMSI PhD program (deadline: Dec 1). If you are a University of Michigan student and are interested in doing research with me, you are welcome to email me and include your interests, major, degree, and resume/CV.
Potential postdocs: I am a faculty mentor through the MIDAS Data Science Fellows program
I was on the job market 2018-19. My job market materials are available here: Research, Teaching, Diversity statement
Teaching
Winter 2020-2023. SI 485 Information Analytics Projects course
Fall 2019-2022. CMPLXSYS 501 Foundations of Complex Systems. [syllabus]
Fall 2019-2022. CMPLXSYS 501 Foundations of Complex Systems. [syllabus]
Contact
Online:
Find me on Google Scholar, or in person as azjacobs + umich.edu. I almost never tweet as @az_jacobs or verb at mastodon as @az_jacobs.
Find me on Google Scholar, or in person as azjacobs + umich.edu. I almost never tweet as @az_jacobs or verb at mastodon as @az_jacobs.
Offline:
I often go by Abbie and as a consequence will respond to Abby, Abbi, Aby, and other creative variations of Abigail.
I often go by Abbie and as a consequence will respond to Abby, Abbi, Aby, and other creative variations of Abigail.