Fairness and ethics in computing — Arvind Narayanan

Publications

AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference
Arvind Narayanan, Sayash Kapoor
Princeton University Press, 2024.

A safe harbor for AI evaluation and red teaming
Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng-Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson
International Conference on Machine Learning (ICML), 2024.

Foundation Model Transparency Reports
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Betty Xiong, Sayash Kapoor, Nestor Maslej, Arvind Narayanan, Percy Liang
Manuscript, 2024.

Promises and pitfalls of artificial intelligence for legal applications
Sayash Kapoor, Peter Henderson, Arvind Narayanan
Cross-disciplinary research in computational law (forthcoming)

Fairness and machine learning: limitations and opportunities
Solon Barocas, Moritz Hardt, Arvind Narayanan.
MIT Press, 2023.
Related: 21 definitions of fairness and their politics. Talk at FAT* 2018.

The limitations of machine learning models for predicting scientific replicability
Molly J. Crockett, Xuechunzi Bai, Sayash Kapoor, Lisa Messeri, Arvind Narayanan.
Proceedings of the National Academies of Sciences, 2023.

How to Prepare for the Deluge of Generative AI on Social Media
Sayash Kapoor, Arvind Narayanan
Knight First Amendment Institute, 2023.

Understanding Social Media Recommendation Algorithms
Arvind Narayanan.
Knight First Amendment Institute, 2023.

Manipulative tactics are the norm in political emails: Evidence from 300K emails from the 2020 U.S. election cycle
Arunesh Mathur, Angelina Wang, Carsten Schwemmer, Maia Hamin, Brandon M. Stewart, Arvind Narayanan.
Big Data & Society, 2023.
Corpus.

The limits of the quantitative approach to discrimination
Arvind Narayanan.
James Baldwin Lecture [transcript], Princeton University, 2022.

Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy
Angelina Wang, Sayash Kapoor, Solon Barocas, Arvind Narayanan.
ACM Journal on Responsible Computing 2023.
Also presented at the ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2023.

How Algorithms Shape the Distribution of Political Advertising: Case Studies of Facebook, Google, and TikTok
Orestis Papakyriakopoulos, Christelle Tessono, Arvind Narayanan, Mihir Kshirsagar.
AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2022.

The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, Arvind Narayanan.
AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES), 2022.

Mitigating dataset harms requires stewardship: Lessons from 1000 papers
Kenny Peng, Arunesh Mathur, Arvind Narayanan.
Neural Information Processing Systems (NeurIPS) 2021.
Oral presentation.

Simulation as Experiment: An Empirical Critique of Simulation Research on Recommender Systems
Amy A. Winecoff, Matthew Sun, Eli Lucherini, Arvind Narayanan.
Workshop on Simulation Methods for Recommender Systems (SimuRec) 2021

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Angelina Wang, Alexander Liu, Ryan Zhang, Anat Kleiman, Leslie Kim, Dora Zhao, Iroha Shirai, Arvind Narayanan, Olga Russakovsky.
International Journal of Computer Vision, 2022.
Extended version of a paper that appeared at ECCV 2020.

T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems
Eli Lucherini, Matthew Sun, Amy Winecoff, Arvind Narayanan.
Manuscript, 2021.

REVISE: A Tool for Measuring and Mitigating Bias in Image Datasets
Angelina Wang, Arvind Narayanan, Olga Russakovsky.
European Conference on Computer Vision (ECCV) 2020.
Spotlight presentation.

Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites
Arunesh Mathur, Gunes Acar, Michael Friedman, Elena Lucherini, Jonathan Mayer, Marshini Chetty, Arvind Narayanan.
ACM Conference on Computer-Supported Cooperative Work and Social Computing, 2019.
FPF Privacy Papers for Policy Makers Award.

Robust de-anonymization of large sparse datasets: a decade later
Arvind Narayanan, Vitaly Shmatikov.
Manuscript, 2019.

Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping
Noah Apthorpe, Danny Yuxing Huang, Dillon Reisman, Arvind Narayanan, Nick Feamster.
Proceedings on Privacy Enhancing Technologies (PoPETs) 2019.

Identification of anonymous DNA using genealogical triangulation
Paul Ellenbogen, Arvind Narayanan.
Manuscript, 2019.

Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest
Arunesh Mathur, Arvind Narayanan, Marshini Chetty.
ACM Conference on Computer-Supported Cooperative Work and Social Computing 2018.
Best paper award. Blog post.

Privacy, ethics, and data access: A case study of the Fragile Families Challenge
Ian Lundberg, Arvind Narayanan, Karen Levy, Matthew J. Salganik.
Socius 2019.
Blog post.

When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies
Steven Goldfeder, Harry Kalodner, Dillon Reisman, Arvind Narayanan.
Proceedings on Privacy Enhancing Technologies (PoPETs) 2018.
Blog post.

Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan.
Science, 2017.
Open access author copy.

Ten simple rules for responsible big data research
Matthew Zook, Solon Barocas, danah boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara A. Koenig, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, Frank Pasquale.
PLoS Computational Biology 2017.

De-anonymizing Web Browsing Data with Social Networks
Jessica Su, Ansh Shukla, Sharad Goel, Arvind Narayanan.
World Wide Web Conference 2017.

When coding style survives compilation: De-anonymizing programmers from executable binaries
Aylin Caliskan, Fabian Yamaguchi, Edwin Dauber, Richard Harang, Konrad Rieck, Rachel Greenstadt, Arvind Narayanan.
Network and Distributed System Security Symposium 2018.
Blog post.

No Encore for Encore? Ethical Questions for Web-Based Censorship Measurement
Arvind Narayanan, Bendert Zevenbergen.
Technology Science, 2015.

A Precautionary Approach to Big Data Privacy
Arvind Narayanan, Joanna Huey, Edward Felten.
Computers, Privacy & Data Protection 2015.
FPF Privacy Papers for Policy Makers Award. Talk video / Blog post.

De-anonymizing Programmers via Code Stylometry
Aylin Caliskan-Islam, Richard Harang, Andrew Liu, Arvind Narayanan, Clare Voss, Fabian Yamaguchi, Rachel Greenstadt.
USENIX security symposium, 2015.
Blog post.

Redefining Genomic Privacy: Trust and Empowerment
Yaniv Erlich, James B Williams, David Glazer, Kenneth Yocum, Nita Farahany, Maynard Olson, Arvind Narayanan, Lincoln D Stein, Jan A Witkowski, Robert C Kain
PLoS Biology, 2014.

No silver bullet: De-identification still doesn’t work
Arvind Narayanan, Edward W. Felten.
Manuscript, 2014.

Why software engineering courses should include ethics coverage
Arvind Narayanan, Shannon Vallor.
Communications of the ACM, 2014.

Routes for breaching and protecting genetic privacy
Yaniv Erlich, Arvind Narayanan.
Nature Reviews Genetics, 2014.
PDF: Sci-Hub | Author manuscript.

33 Bits of Entropy: Myths and Fallacies of “Personally Identifiable Information”
Arvind Narayanan.
Volume 1 of Tiny Transactions on Computer Science, 2012. ("The premier venue for computer science research of 140 characters or less.")

On the Feasibility of Internet-Scale Author Identification
Arvind Narayanan, Hristo Paskov, Neil Gong, John Bethencourt, Emil Stefanov, Richard Shin, Dawn Song.
IEEE Security and Privacy (Oakland) 2012.
Blog post

An Adversarial Analysis of the Reidentifiability of the Heritage Health Prize Dataset.
Arvind Narayanan.
Manuscript, 2011.

“You Might Also Like:” Privacy Risks of Collaborative Filtering
Joseph A. Calandrino, Ann Kilzer, Arvind Narayanan, Edward W. Felten, Vitaly Shmatikov.
IEEE Security and Privacy (Oakland) 2011.
Blog post.

Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge
Arvind Narayanan, Elaine Shi, Benjamin I. P. Rubinstein.
International Joint Conference on Neural Networks 2011.
Blog post.

Myths and Fallacies of “Personally Identifiable Information”
Arvind Narayanan, Vitaly Shmatikov.
Communications of the ACM, 2010.
Blog post.

De-anonymizing Social Networks
Arvind Narayanan, Vitaly Shmatikov.
IEEE Security and Privacy 2009.
Blog post.

Robust De-anonymization of Large Sparse Datasets
Arvind Narayanan, Vitaly Shmatikov.
IEEE Security and Privacy (Oakland) 2008.
2008 Privacy Enhancing Technologies award. 2019 IEEE Security & Privacy Test of Time Award.

AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference
Arvind Narayanan, Sayash Kapoor
Princeton University Press, 2024.