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Using Technical Solutions to Address Issues in Privacy Law: A Talk by Professor Zubair Shafiq

Posted By Devon Siebels, Nov 9, 2023

On November 13th, King Hall’s Center for Innovation, Law, and Society will host a talk by UC Davis Professor of Computer Science Zubair Shafiq. Professor Shafiq’s research focuses on the implementation of computer science solutions and frameworks to help make the internet more private and secure.

            Professor Shafiq’s recent research examines high-profile privacy issues such as a detailed analysis of the data collected and used by Amazon’s smart speaker network, as well as an audit of YouTube’s recommendation system to evaluate the extent the system drives radicalization.^[1]  Professor Shafiq’s research also touches on exploring technical solutions to privacy problems more generally, including an analysis of the trade-off between accuracy and privacy in ensemble machine learning systems and an exploration of how end-users can block the privacy-compromising effects of JavaScript without compromising the functionality of webpages.^[2]

            Professor Shafiq works to shed light on these opaque systems and evaluate their compliance with privacy principles, such as the right to know when personal information is being collected and how that information is being shared. These evaluations are necessary to identify the most significant harms large technology firms inflict upon the public by providing evidence of privacy harms distributed across a complex network. This field of law has issues enforcing action against these sorts of networked harms, making their proper identification essential.^[3] To regulate these invasive, automated systems, we must have the tools to understand exactly how those laws function, and Professor Shafiq’s work provides an excellent template for how to develop and apply these tools.

            Recently implemented state privacy laws attempt to address the lack of transparency with user information in automated systems. Both Colorado and Virginia have implemented new rules mandating the allowance of users to opt out of automated decision-making and requiring regular impact assessments to evaluate the harm of these systems.^[4] New York City’s recently adopted rule regarding “Automatic Employment Decisions” targets the potential for discrimination brought about by the use of AI in hiring and represents a new attempt to address these issues with greater specificity.^[5] With industry leaders calling for the federal regulation of generative AI programs like Chat GPT, the future of privacy law will be better served by incorporating technical solutions like those examined by Professor Shafiq.^[6]

As recent privacy legislation begins to address technical systems like Machine Learning and AI^[7], incorporating solutions from the realm of computer science into the law has the potential to successfully balance privacy interests without compromising the effectiveness of these groundbreaking technologies. Professor Shafiq’s research has the potential to provide the foundation for an effective road map both for technology firms and policymakers as privacy law continues to develop.

[1] Umar Iqbal & Zubair Shafiq et. al., Tracking, Profiling, and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem 569-83 (IMC ’23: Proceedings of the 2023 ACM Internet Measurement Conf., 2023), https://doi.org/10.1145/3618257.3624803; Muhammad Haroon & Zubair Shafiq et. al, Auditing YouTube's Recommendation System for Ideologically Congenial, Extreme, and Problematic Recommendations, OSF (Jun. 14 2023), https://osf.io/gvsk5/.

[2] Shahbaz Rezaei, Zubair Shafiq & Xin Liu, Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective, 2023 IEEE Symposium on Security and Privacy, 364-81 (2023), https://web.cs.ucdavis.edu/~Zubair/files/ensemble-sp2023.pdf; Abdul Haddi Amjad, Zubair Shafiq & and Muhammad Ali Gulzar, Blocking JavaScript without Breaking the Web: An Empirical Investigation, Privacy Enhancing Technologies Symposium 391-404 (2023), https://petsymposium.org/popets/2023/popets-2023-0087.pdf.

[3] Peter Ormerod, Privacy Qui Tam, 98 Notre Dame L. Rev. 267, 279–80 (2022) (discussing the critiques of two litigation approaches to the networked harms manifesting at scale).

[4] Which States Have Consumer Data Privacy Laws?, Bloomberg Law (September 7, 2023), https://pro.bloomberglaw.com/brief/state-privacy-legislation-tracker/.

[5] Steve Lohr, A Hiring Law Blazes a Path for A.I. Regulation, N.Y. Times (May 25, 2023), https://www.nytimes.com/2023/05/25/technology/ai-hiring-law-new-york.html.

[6] Cecilia Kang, OpenAI’s Sam Altman Urges A.I. Regulation in Senate Hearing, N.Y. Times (May 16, 2023), https://www.nytimes.com/2023/05/16/technology/openai-altman-artificial-intelligence-regulation.html.

[7] Cecilia Kang and David E. Sanger, Biden Issues Executive Order to Create A.I. Safeguards, N.Y. Times (Oct. 30, 2023), https://www.nytimes.com/2023/10/30/us/politics/biden-ai-regulation.html.