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.