Proceedings of the 2022 Symposium on Computer Science and Law最新文献_第2页

Algorithmic Learning Foundations for Common Law 普通法的算法学习基础

Proceedings of the 2022 Symposium on Computer Science and Law Pub Date : 2022-09-07 DOI: 10.1145/3511265.3550438

Jason D. Hartline, Daniel W. Linna, Liren Shan, Alex Tang

引用次数: 1

Classification Protocols with Minimal Disclosure 最小披露的分类协议

Proceedings of the 2022 Symposium on Computer Science and Law Pub Date : 2022-09-06 DOI: 10.1145/3511265.3550442

Jinshuo Dong, Jason D. Hartline, Aravindan Vijayaraghavan

引用次数: 1

Can the Government Compel Decryption?: Don't Trust - Verify 政府能强制解密吗?:不要相信-验证

Proceedings of the 2022 Symposium on Computer Science and Law Pub Date : 2022-08-04 DOI: 10.1145/3511265.3550441

A. Cohen, Sarah Scheffler, Mayank Varia

{"title":"Can the Government Compel Decryption?: Don't Trust - Verify","authors":"A. Cohen, Sarah Scheffler, Mayank Varia","doi":"10.1145/3511265.3550441","DOIUrl":"https://doi.org/10.1145/3511265.3550441","url":null,"abstract":"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 the answer to this question. The Holy Grail of this line of work would be a framework for reasoning about whether the testimony implicit in any action is already known to the government. In this paper we attempt something narrower. We introduce a framework for specifying actions for which all implicit testimony is, constructively, a foregone conclusion. Our approach is centered around placing the burden of proof on the government to demonstrate that it is not \"rely[ing] on the truthtelling\" of the respondent. Building on original legal analysis and using precise computer science formalisms, we propose demonstrability as a new central concept for describing compelled acts. We additionally provide a language for whether a compelled action meaningfully entails the respondent to perform in a manner that is 'as good as' the government's desired goal. Then, we apply our definitions to analyze the compellability of several cryptographic primitives including decryption, multifactor authentication, commitment schemes, and hash functions. In particular, our framework reaches a novel conclusion about compelled decryption in the setting that the encryption scheme is deniable: the government can compel but the respondent is free to use any password of her choice.","PeriodicalId":254114,"journal":{"name":"Proceedings of the 2022 Symposium on Computer Science and Law","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123366754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Programming Languages and Law: A Research Agenda 程序设计语言与法律:一个研究议程

Proceedings of the 2022 Symposium on Computer Science and Law Pub Date : 2022-06-29 DOI: 10.1145/3511265.3550447

James Grimmelmann

引用次数: 1

Non-Determinism and the Lawlessness of Machine Learning Code 机器学习代码的非决定论和无法无天

Proceedings of the 2022 Symposium on Computer Science and Law Pub Date : 2022-06-23 DOI: 10.1145/3511265.3550446

A. Cooper, Jonathan Frankle, Chris De Sa

{"title":"Non-Determinism and the Lawlessness of Machine Learning Code","authors":"A. Cooper, Jonathan Frankle, Chris De Sa","doi":"10.1145/3511265.3550446","DOIUrl":"https://doi.org/10.1145/3511265.3550446","url":null,"abstract":"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, stochasticity and non-determinism. While some recent work has begun to reason about the relationship between stochasticity and arbitrariness in legal contexts, the role of non-determinism more broadly remains unexamined. In this paper, we clarify the overlap and differences between these two concepts, and show that the effects of non-determinism, and consequently its implications for the law, become clearer from the perspective of reasoning about ML outputs as distributions over possible outcomes. This distributional viewpoint accounts for randomness by emphasizing the possible outcomes of ML. Importantly, this type of reasoning is not exclusive with current legal reasoning; it complements (and in fact can strengthen) analyses concerning individual, concrete outcomes for specific automated decisions. By illuminating the important role of non-determinism, we demonstrate that ML code falls outside of the cyberlaw frame of treating \"code as law,'' as this frame assumes that code is deterministic. We conclude with a brief discussion of what work ML can do to constrain the potentially harm-inducing effects of non-determinism, and we indicate where the law must do work to bridge the gap between its current individual-outcome focus and the distributional approach that we recommend.","PeriodicalId":254114,"journal":{"name":"Proceedings of the 2022 Symposium on Computer Science and Law","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122957463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity 人类智慧的形式化:著作权法实质相似性的定量框架

Proceedings of the 2022 Symposium on Computer Science and Law Pub Date : 2022-06-02 DOI: 10.1145/3511265.3550444

Sarah Scheffler, Eran Tromer, Mayank Varia

{"title":"Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity","authors":"Sarah Scheffler, Eran Tromer, Mayank Varia","doi":"10.1145/3511265.3550444","DOIUrl":"https://doi.org/10.1145/3511265.3550444","url":null,"abstract":"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-defined, contradictory, or internally-inconsistent. This work suggests that key parts of the substantial-similarity puzzle are amenable to modeling inspired by theoretical computer science. Our proposed framework quantitatively evaluates how much ''novelty'' is needed to produce the derived work with access to the original work, versus reproducing it without access to the copyrighted elements of the original work. ''Novelty'' is captured by a computational notion of description length, in the spirit of Kolmogorov-Levin complexity, which is robust to mechanical transformations and availability of contextual information. This results in an actionable framework that could be used by courts as an aid for deciding substantial similarity. We evaluate it on several pivotal cases in copyright law and observe that the results are consistent with the rulings, and are philosophically aligned with the abstraction-filtration-comparison test of Altai.","PeriodicalId":254114,"journal":{"name":"Proceedings of the 2022 Symposium on Computer Science and Law","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131463658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy 超越广告:法律和公共政策中的顺序决策算法

Proceedings of the 2022 Symposium on Computer Science and Law Pub Date : 2021-12-13 DOI: 10.1145/3511265.3550439

Peter Henderson, Ben Chugg, Brandon R. Anderson, Daniel E. Ho

{"title":"Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy","authors":"Peter Henderson, Ben Chugg, Brandon R. Anderson, Daniel E. Ho","doi":"10.1145/3511265.3550439","DOIUrl":"https://doi.org/10.1145/3511265.3550439","url":null,"abstract":"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 private sector (e.g., online advertising), the tendency to naively apply algorithms motivated by one domain, often online advertisements, can be called the ''advertisement fallacy.'' Our main thesis is that law and public policy pose distinct methodological challenges that the machine learning community has not yet addressed. Machine learning will need to address these methodological problems to move ''beyond ads.'' Public law, for instance, can pose multiple objectives, necessitate batched and delayed feedback, and require systems to learn rational, causal decision-making policies, each of which presents novel questions at the research frontier. We discuss a wide range of potential applications of sequential decision-making algorithms in regulation and governance, including public health, environmental protection, tax administration, occupational safety, and benefits adjudication. We use these examples to highlight research needed to render sequential decision making policy-compliant, adaptable, and effective in the public sector. We also note the potential risks of such deployments and describe how sequential decision systems can also facilitate the discovery of harms. We hope our work inspires more investigation of sequential decision making in law and public policy, which provide unique challenges for machine learning researchers with potential for significant social benefit.","PeriodicalId":254114,"journal":{"name":"Proceedings of the 2022 Symposium on Computer Science and Law","volume":"50 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120810970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Proceedings of the 2022 Symposium on Computer Science and Law 2022年计算机科学与法律研讨会论文集

Proceedings of the 2022 Symposium on Computer Science and Law Pub Date : 1900-01-01 DOI: 10.1145/3511265

引用次数: 0