It is our pleasure to welcome you to the 2022 ACM Symposium on Computer Science and Law - CSLAW'22. Building upon the success of the 2019 Inaugural ACM CSLAW Symposium and that of several computer science and law events that were held in 2020 and 2021, we and General Chair Daniel J. Weitzner proposed in September 2021 that the ACM establish a symposium series in this burgeoning field, starting with a 2022 symposium, and were delighted when our proposal was approved.
The mission of CSLAW'22 is to present both original research papers and systematizations of knowledge that address problems in a truly interdisciplinary fashion. The call for papers solicited proposed solutions that, rather than simply applying computer science to law or legal analysis to computer-related technologies, strove to integrate the two disciplines in a way that surpasses what either could accomplish by itself.
Proceeding Downloads
Non-Determinism and the Lawlessness of Machine Learning Code
Legal literature on machine learning (ML) tends to focus on harms, and thus tends to reason about individual model outcomes and summary error rates. This focus has masked important aspects of ML that are rooted in its reliance on randomness --- namely, ...
Using Zero-Knowledge to Reconcile Law Enforcement Secrecy and Fair Trial Rights in Criminal Cases
The use of hidden investigative software to collect evidence of crimes presents courts with a recurring dilemma: On the one hand, there is often clear public interest in keeping the software hidden to preserve its effectiveness in fighting crimes. On ...
Can the Government Compel Decryption?: Don't Trust - Verify
If a court knows that a respondent knows the password to a device, can the court compel the respondent to enter that password into the device? In this work, we propose a new approach to the foregone conclusion doctrine from Fisher v. U.S. that governs ...
Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity
A central notion in U.S. copyright law is judging the substantial similarity between an original and an (allegedly) derived work. Capturing this notion has proven elusive, and the many approaches offered by case law and legal scholarship are often ill-...
Bridging the Computer Science -- Law Divide: Recommendations from the Front Lines
Many pressing societal questions can be answered only by bringing experts from different disciplines together. Questions around misinformation and disinformation, platform power, surveillance capitalism, information privacy, and algorithmic bias, among ...
Multi-Regulation Computing: Examining the Legal and Policy Questions That Arise From Secure Multiparty Computation
This work examines privacy laws and regulations that limit disclosure of personal data, and explores whether and how these restrictions apply when participants use cryptographically secure multi-party computation (MPC). By protecting data during use, ...
Classification Protocols with Minimal Disclosure
We consider multi-party protocols for classification that are motivated by applications such as e-discovery in court proceedings. We identify a protocol that guarantees that the requesting party receives all responsive documents and the sending party ...
The Privacy-Fairness-Accuracy Frontier: A Computational Law & Economics Toolkit for Making Algorithmic Tradeoffs
Both law and computer science are concerned with developing frameworks for protecting privacy and ensuring fairness. Both fields often consider these two values separately and develop legal doctrines and machine learning metrics in isolation from one ...
Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy
We explore the promises and challenges of employing sequential decision-making algorithms -- such as bandits, reinforcement learning, and active learning -- in law and public policy. While such algorithms have well-characterized performance in the ...
Blind Justice: Algorithms and Neutrality in the Case of Redistricting
In several areas of law and public policy, there have been longstanding dreams that computers can secure decisionmaking that takes only some things into account, while remaining demonstrably neutral to other factors. In 2022, the U.S. Supreme Court will ...
Algorithmic Learning Foundations for Common Law
This paper looks at a common law legal system as a learning algorithm, models specific features of legal proceedings, and asks whether this system learns efficiently. A particular feature of our model is explicitly viewing various aspects of court ...
The Case for Establishing a Collective Perspective to Address the Harms of Platform Personalization
Personalization on digital platforms drives a broad range of harms, including misinformation, manipulation, social polarization, subversion of autonomy, and discrimination. In recent years, policymakers, civil society advocates, and researchers have ...
Some Misconceptions about Software in the Copyright Literature
The technical complexity and functionality of computer programs have made it difficult for courts to apply conventional copyright concepts, such as the idea/expression distinction, in the software copyright case law. This has created fertile ground for ...
Toward Architecture-Driven Interdisciplinary Research: Learnings from a Case Study of COVID-19 Contact Tracing Apps
This paper explores the use of an architectural perspective to study complex data ecosystems and to facilitate a normative discourse on such ecosystems. It argues that an architectural perspective is helpful to bridging discursive and methodological ...
Programming Languages and Law: A Research Agenda
If code is law, then the language of law is a programming language. Lawyers and legal scholars can learn about law by studying programming-language theory, and programming-language tools can be usefully applied to legal problems. This article surveys ...
Cryptography, Trust and Privacy: It's Complicated
Privacy technologies support the provision of online services while protecting user privacy. Cryptography lies at the heart of many such technologies, creating remarkable possibilities in terms of functionality while offering robust guarantees of data ...
Redress for Dark Patterns Privacy Harms? A Case Study on Consent Interactions
Internet users are constantly subjected to incessant demands for attention in a noisy digital world. Countless inputs compete for the chance to be clicked, to be seen, and to be interacted with, and they can deploy tactics that take advantage of ...
Index Terms
- Proceedings of the 2022 Symposium on Computer Science and Law