Publications

@article{hwang_trust_2025,
	title = {Trust and {Friction}: {Negotiating} {How} {Information} {Flows} {Through} {Decentralized} {Social} {Media}},
	url = {https://arxiv.org/pdf/2503.02150},
	journal = {Forthcoming the 28th ACM SIGCHI Conference on Computer-Supported Cooperative Work \& Social Computing (CSCW)},
	author = {Hwang, Sohyeon and Nanayakkara, Priyanka and Shvartzshnaider, Yan},
	year = {2025},
}

@article{choksi_student_2024,
	title = {Student {Privacy} {Activism}: {Protesting} {Radical} {Digital} {Transformation} in {EdTech}},
	volume = {14},
	issn = {2158-3897, 2381-5892},
	shorttitle = {Student {Privacy} {Activism}},
	url = {https://scholarlypublishingcollective.org/information-policy/article/doi/10.5325/jinfopoli.14.2024.0017/390867/Student-Privacy-Activism-Protesting-Radical},
	doi = {10.5325/jinfopoli.14.2024.0017},
	abstract = {Abstract 
            Student-organized activism and obfuscation respond to intrusive surveillance in digital assessment within higher education. This article explores privacy surveillance disconnects and the emergence of protests against antinormative practices. Employing qualitative and quantitative methods, including content analysis of subreddits focused on higher education, student privacy, and specific university campus communities, the study considers multiple stakeholders’ perspectives. The findings illustrate the creative avenues students have adopted to counter online assessment tools. Emphasizing the significance of privacy and autonomy in higher education, this work sheds light on the challenges faced by students and provides insights into their strategies for addressing privacy concerns.},
	language = {en},
	urldate = {2024-10-10},
	journal = {Journal of Information Policy},
	author = {Choksi, Madiha Z. and Ali, Amna Farzand and Shvartzshnaider, Yan and Sanfilippo, Madelyn Rose},
	month = sep,
	year = {2024},
}

Abstract Student-organized activism and obfuscation respond to intrusive surveillance in digital assessment within higher education. This article explores privacy surveillance disconnects and the emergence of protests against antinormative practices. Employing qualitative and quantitative methods, including content analysis of subreddits focused on higher education, student privacy, and specific university campus communities, the study considers multiple stakeholders’ perspectives. The findings illustrate the creative avenues students have adopted to counter online assessment tools. Emphasizing the significance of privacy and autonomy in higher education, this work sheds light on the challenges faced by students and provides insights into their strategies for addressing privacy concerns.

@inproceedings{choksi_emerging_2024,
	address = {New York, NY, USA},
	series = {{FAccT} '24},
	title = {The {Emerging} {Artifacts} of {Centralized} {Open}-{Code}},
	isbn = {9798400704505},
	url = {https://dl.acm.org/doi/10.1145/3630106.3659019},
	doi = {10.1145/3630106.3659019},
	abstract = {In 2022, generative model based coding assistants became widely available with the public release of GitHub Copilot. Approaches to generative coding are often critiqued within the context of advances in machine learning. We argue that tools such as Copilot are better understood when contextualized against technologies derived from the same communities and datasets. Our work traces the historical and ideological origins of free and open source code and characterizes the process of centralization. We examine three case studies —Dependabot, Crater, and Copilot— to compare the engineering, social, and legal qualities of technical artifacts derived from shared community-based labor. Our analysis focuses on the implications these artifacts create for infrastructural dependencies, community adoption, and intellectual property. Reframing generative coding assistants through a set of peer technologies broadens considerations for academics and policymakers beyond machine learning, to include the ways technical artifacts are derived from communities.},
	urldate = {2024-06-08},
	booktitle = {Proceedings of the 2024 {ACM} {Conference} on {Fairness}, {Accountability}, and {Transparency}},
	publisher = {Association for Computing Machinery},
	author = {Choksi, Madiha Zahrah and Mandel, Ilan and Widder, David and Shvartzshnaider, Yan},
	month = jun,
	year = {2024},
	keywords = {Artificial Intelligence, Commons, Ethics, Free Software, Governance, Licenses, Political Economy},
	pages = {1971--1983},
}

In 2022, generative model based coding assistants became widely available with the public release of GitHub Copilot. Approaches to generative coding are often critiqued within the context of advances in machine learning. We argue that tools such as Copilot are better understood when contextualized against technologies derived from the same communities and datasets. Our work traces the historical and ideological origins of free and open source code and characterizes the process of centralization. We examine three case studies —Dependabot, Crater, and Copilot— to compare the engineering, social, and legal qualities of technical artifacts derived from shared community-based labor. Our analysis focuses on the implications these artifacts create for infrastructural dependencies, community adoption, and intellectual property. Reframing generative coding assistants through a set of peer technologies broadens considerations for academics and policymakers beyond machine learning, to include the ways technical artifacts are derived from communities.

@article{brehm_understanding_2023,
	title = {Understanding {Privacy} in {Virtual} {Reality} {Classrooms}: {A} {Contextual} {Integrity} {Perspective}},
	issn = {1540-7993, 1558-4046},
	shorttitle = {Understanding {Privacy} in {Virtual} {Reality} {Classrooms}},
	url = {https://ieeexplore.ieee.org/document/10352441/},
	doi = {10.1109/MSEC.2023.3336802},
	urldate = {2023-12-12},
	journal = {IEEE Security \& Privacy},
	author = {Brehm, Karoline and Shvartzhnaider, Yan},
	year = {2023},
	pages = {2--11},
}

@inproceedings{shvartzshanider-etal-2023-beyond,
	address = {Singapore},
	title = {Beyond the text: {Analysis} of privacy statements through syntactic and semantic role labeling},
	url = {https://aclanthology.org/2023.nllp-1.10},
	abstract = {This paper formulates a new task of extracting privacy parameters from a privacy policy, through the lens of Contextual Integrity (CI), an established social theory framework for reasoning about privacy norms. Through extensive experiments, we further show that incorporating CI-based domain-specific knowledge into a BERT-based SRL model results in the highest precision and recall, achieving an F1 score of 84\%. With our work, we would like to motivate new research in building NLP applications for the privacy domain.},
	booktitle = {Proceedings of the natural legal language processing workshop 2023},
	publisher = {Association for Computational Linguistics},
	author = {Shvartzshanider, Yan and Balashankar, Ananth and Wies, Thomas and Subramanian, Lakshminarayanan},
	month = dec,
	year = {2023},
	pages = {85--98},
}

This paper formulates a new task of extracting privacy parameters from a privacy policy, through the lens of Contextual Integrity (CI), an established social theory framework for reasoning about privacy norms. Through extensive experiments, we further show that incorporating CI-based domain-specific knowledge into a BERT-based SRL model results in the highest precision and recall, achieving an F1 score of 84%. With our work, we would like to motivate new research in building NLP applications for the privacy domain.

@article{mcguigan_private_2023,
	title = {Private attributes: {The} meanings and mechanisms of “privacy-preserving” adtech},
	volume = {0},
	url = {https://doi.org/10.1177/14614448231213267},
	doi = {10.1177/14614448231213267},
	language = {en},
	number = {0},
	journal = {New Media \& Society},
	author = {McGuigan, L. and Sivan-Sevilla, I. and Parham, P. and Shvartzshnaider, Y.},
	year = {2023},
}

@article{sanfilippo_privacy_2023,
	title = {Privacy governance not included: analysis of third parties in learning management systems},
	volume = {ahead-of-print},
	issn = {2398-5348},
	shorttitle = {Privacy governance not included},
	url = {https://doi.org/10.1108/ILS-04-2023-0033},
	doi = {10.1108/ILS-04-2023-0033},
	abstract = {Purpose This paper aims to address research gaps around third party data flows in education by investigating governance practices in higher education with respect to learning management system (LMS) ecosystems. The authors answer the following research questions: how are LMS and plugins/learning tools interoperability (LTI) governed at higher education institutions? Who is responsible for data governance activities around LMS? What is the current state of governance over LMS? What is the current state of governance over LMS plugins, LTI, etc.? What governance issues are unresolved in this domain? How are issues of privacy and governance regarding LMS and plugins/LTIs documented or communicated to the public and/or community members? Design/methodology/approach This study involved three components: (1) An online questionnaire about LMS, plugin and LTI governance practices from information technology professionals at seven universities in the USA (n = 4) and Canada (n = 3). The responses from these individuals helped us frame and design the interview schedule. (2) A review of public data from 112 universities about LMS plugin and LTI governance. Eighteen of these universities provide additional documentation, which we analyze in further depth. (3) A series of extensive interviews with 25 university data governance officers with responsibilities for LMS, plugin and/or LTI governance, representing 14 different universities. Findings The results indicate a portrait of fragmented and unobtrusive, unnoticed student information flows to third parties. From coordination problems on individual college campuses to disparate distributions of authority across campuses, as well as from significant data collection via individual LTIs to a shared problem of scope across many LTIs, the authors see that increased and intentional governance is needed to improve the state of student privacy and provide transparency in the complex environment around LMSs. Yet, the authors also see that there are logical paths forward based on successful governance and leveraging existing collaborative networks among data governance professionals in higher education. Originality/value Substantial prior work has examined issues of privacy in the education context, although little research has directly examined higher education institutions’ governance practices of LMS, plugin and LTI ecosystems. The tight integration of first and third-party tools in this ecosystem raises concerns that student data may be accessed and shared without sufficient transparency or oversight and in violation of established education privacy norms. However, these technologies and the university governance practices that could check inappropriate data handling remain under-scrutinized. This paper addresses this gap by investigating the governance practices of higher education institutions with respect to LMS ecosystems.},
	number = {ahead-of-print},
	urldate = {2023-09-27},
	journal = {Information and Learning Sciences},
	author = {Sanfilippo, Madelyn Rose and Apthorpe, Noah and Brehm, Karoline and Shvartzshnaider, Yan},
	month = jan,
	year = {2023},
	keywords = {Computing/technology policy, Educational technology, Empirical studies in social computing, Learning management systems, Privacy, Third-party data controllers},
}

Purpose This paper aims to address research gaps around third party data flows in education by investigating governance practices in higher education with respect to learning management system (LMS) ecosystems. The authors answer the following research questions: how are LMS and plugins/learning tools interoperability (LTI) governed at higher education institutions? Who is responsible for data governance activities around LMS? What is the current state of governance over LMS? What is the current state of governance over LMS plugins, LTI, etc.? What governance issues are unresolved in this domain? How are issues of privacy and governance regarding LMS and plugins/LTIs documented or communicated to the public and/or community members? Design/methodology/approach This study involved three components: (1) An online questionnaire about LMS, plugin and LTI governance practices from information technology professionals at seven universities in the USA (n = 4) and Canada (n = 3). The responses from these individuals helped us frame and design the interview schedule. (2) A review of public data from 112 universities about LMS plugin and LTI governance. Eighteen of these universities provide additional documentation, which we analyze in further depth. (3) A series of extensive interviews with 25 university data governance officers with responsibilities for LMS, plugin and/or LTI governance, representing 14 different universities. Findings The results indicate a portrait of fragmented and unobtrusive, unnoticed student information flows to third parties. From coordination problems on individual college campuses to disparate distributions of authority across campuses, as well as from significant data collection via individual LTIs to a shared problem of scope across many LTIs, the authors see that increased and intentional governance is needed to improve the state of student privacy and provide transparency in the complex environment around LMSs. Yet, the authors also see that there are logical paths forward based on successful governance and leveraging existing collaborative networks among data governance professionals in higher education. Originality/value Substantial prior work has examined issues of privacy in the education context, although little research has directly examined higher education institutions’ governance practices of LMS, plugin and LTI ecosystems. The tight integration of first and third-party tools in this ecosystem raises concerns that student data may be accessed and shared without sufficient transparency or oversight and in violation of established education privacy norms. However, these technologies and the university governance practices that could check inappropriate data handling remain under-scrutinized. This paper addresses this gap by investigating the governance practices of higher education institutions with respect to LMS ecosystems.

@incollection{sanfilippo_technofuturism_2023,
	title = {Technofuturism in {Play}},
	booktitle = {Governing {Smart} {Cities} as {Knowledge} {Commons}},
	publisher = {Cambridge University Press},
	author = {Sanfilippo, Madelyn Rose and Shvartzshnaider, Yan},
	year = {2023},
	pages = {223},
}

@inproceedings{zhang_stop_2022,
	address = {New York, NY, USA},
	series = {{FAccT} '22},
	title = {Stop the {Spread}: {A} {Contextual} {Integrity} {Perspective} on the {Appropriateness} of {COVID}-19 {Vaccination} {Certificates}},
	isbn = {9781450393522},
	shorttitle = {Stop the {Spread}},
	url = {https://doi.org/10.1145/3531146.3533222},
	doi = {10.1145/3531146.3533222},
	abstract = {We present an empirical study exploring how privacy influences the acceptance of vaccination certificate (VC) deployments across different realistic usage scenarios. The study employed the privacy framework of Contextual Integrity, which has been shown to be particularly effective in capturing people’s privacy expectations across different contexts. We use a vignette methodology, where we selectively manipulate salient contextual parameters to learn whether and how they affect people’s attitudes towards VCs. We surveyed 890 participants from a demographically-stratified sample of the US population to gauge the acceptance and overall attitudes towards possible VC deployments to enforce vaccination mandates and the different information flows VCs might entail. Analysis of results collected as part of this study is used to derive general normative observations about different possible VC practices and to provide guidance for the possible deployments of VCs in different contexts.},
	urldate = {2022-06-21},
	booktitle = {2022 {ACM} {Conference} on {Fairness}, {Accountability}, and {Transparency}},
	publisher = {Association for Computing Machinery},
	author = {Zhang, Shikun and Shvartzshnaider, Yan and Feng, Yuanyuan and Nissenbaum, Helen and Sadeh, Norman},
	month = jun,
	year = {2022},
	keywords = {contextual integrity, information privacy, privacy norms, vaccination certificates},
	pages = {1657--1670},
}

We present an empirical study exploring how privacy influences the acceptance of vaccination certificate (VC) deployments across different realistic usage scenarios. The study employed the privacy framework of Contextual Integrity, which has been shown to be particularly effective in capturing people’s privacy expectations across different contexts. We use a vignette methodology, where we selectively manipulate salient contextual parameters to learn whether and how they affect people’s attitudes towards VCs. We surveyed 890 participants from a demographically-stratified sample of the US population to gauge the acceptance and overall attitudes towards possible VC deployments to enforce vaccination mandates and the different information flows VCs might entail. Analysis of results collected as part of this study is used to derive general normative observations about different possible VC practices and to provide guidance for the possible deployments of VCs in different contexts.

@article{apthorpe_discovering_2018,
	title = {Discovering smart home internet of things privacy norms using contextual integrity},
	volume = {2},
	url = {https://dl.acm.org/doi/pdf/10.1145/3214262},
	number = {2},
	journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
	author = {Apthorpe, Noah and Shvartzshnaider, Yan and Mathur, Arunesh and Reisman, Dillon and Feamster, Nick},
	year = {2018},
	note = {Publisher: ACM New York, NY, USA},
	keywords = {publications},
	pages = {1--23},
}

@inproceedings{mir_it_2018,
	title = {It {Takes} a {Village}: {A} {Community} {Based} {Participatory} {Framework} for {Privacy} {Design}},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8406569&casa_token=0eUDaIyeHgwAAAAA:alFqssV2228ZVh1yb6HneZ2FOx3nE-lbA5rsLbGHUBHs7ZembEDkM7rfHG7mT8myX5pmW-4ZCC0&tag=1},
	booktitle = {2018 {IEEE} {European} {Symposium} on {Security} and {Privacy} {Workshops} ({EuroS}\&{PW})},
	publisher = {IEEE},
	author = {Mir, Darakhshan J and Shvartzshnaider, Yan and Latonero, Mark},
	year = {2018},
	keywords = {publications},
	pages = {112--115},
}

@inproceedings{shvartzshanider_recipe_2018,
	address = {Melbourne, Australia},
	title = {{RECIPE}: {Applying} {Open} {Domain} {Question} {Answering} to {Privacy} {Policies}},
	shorttitle = {{RECIPE}},
	url = {http://aclweb.org/anthology/W18-2608},
	doi = {10.18653/v1/W18-2608},
	language = {en},
	urldate = {2022-03-30},
	booktitle = {Proceedings of the {Workshop} on {Machine} {Reading} for {Question} {Answering}},
	publisher = {Association for Computational Linguistics},
	author = {Shvartzshanider, Yan and Balashankar, Ananth and Wies, Thomas and Subramanian, Lakshminarayanan},
	year = {2018},
	keywords = {publications},
	pages = {71--77},
}

@inproceedings{shvartzshnaider_learning_2016,
	title = {Learning privacy expectations by crowdsourcing contextual informational norms},
	url = {https://privaci.info/papers/hcomp_paper.pdf},
	booktitle = {Fourth {AAAI} {Conference} on {Human} {Computation} and {Crowdsourcing}},
	author = {Shvartzshnaider, Yan and Tong, Schrasing and Wies, Thomas and Kift, Paula and Nissenbaum, Helen and Subramanian, Lakshminarayanan and Mittal, Prateek},
	year = {2016},
	keywords = {publications},
}