Computer science research sometimes brushes with the law, from red-team exercises that probe the boundaries of authentication mechanisms, to AI research processing copyrighted material, to platform research measuring the behavior of algorithms and users. U.S.-based computer security research is no stranger to the Computer Fraud and Abuse Act (CFAA) and the Digital Millennium Copyright Act (DMCA) in a relationship that is still evolving through case law, research practices, changing policies, and legislation.

Amid the landscape computer scientists, lawyers, and policymakers have learned to navigate, anti-fraud laws are a surprisingly under-examined challenge for computer science research. Fraud brings separate issues that are not addressed by the methods for navigating CFAA, DMCA, and Terms of Service that are more familiar in the computer security literature. Although anti-fraud laws have been discussed to a limited extent in older research on phishing attacks, modern computer science researchers are left with little guidance when it comes to navigating issues of deception outside the context of pure laboratory research.

In this paper, we analyze and taxonomize the anti-fraud and deception issues that arise in several areas of computer science research. We find that, despite the lack of attention to these issues in the legal and computer science literature, issues of misrepresented identity or false information that could implicate anti-fraud laws are actually relevant to many methodologies used in computer science research, including penetration testing, web scraping, user studies, sock puppets, social engineering, auditing AI or socio-technical systems, and attacks on artificial intelligence. We especially highlight the importance of anti-fraud laws in two particular research fields that are of great policy importance in the current moment: attacking or auditing AI systems, and research involving legal identification.

Finally, guided by principles in research ethics, we suggest methods for computer scientists to navigate fraud and identity issues, as well as possible legal paths forward for policymakers to consider.

Data is often thought of and treated as a non-rival good, which would imply that one person’s use of data does not inherently diminish its availability for others. Building on research in privacy and statistics, we argue that there exist many important settings in which data should be treated as a rival good. Our argument takes into account modern uses of data for statistics, machine learning, and a variety of other purposes, in conjunction with requirements of privacy protection and statistical validity. Excessive sharing or reuse of data about individuals can lead to leakage of sensitive personal information, potentially causing harm to those whose information is included in the data. Overuse of data in statistics or machine learning can lead to overfitting, i.e., models that perform well on training data but poorly on fresh unseen data.

Recognizing the rival nature of data offers an opportunity to rethink the way data are managed and used. In an age where the training of AI models generates a massive appetite for data, this perspective has the potential to inform the creation of new regulation and technical infrastructure that will be able to safely and responsibly manage data, track their various uses, and ensure that privacy and statistical usefulness are respected and preserved.

We observe that current EU regulation and existing approaches to science seeking to increase opportunities for data-sharing and reuse of data misconstrue the complex nature of data, inadvertently creating risks of privacy harms and overfitting, hence squandering the societal benefits that can be derived from data.

Recognizing the rival nature of data has implications for policy and practice. Regulation should address the limitations and risks associated with data reuse and facilitate technological measures to track, analyze, and manage data usage with the goal of ensuring that privacy and statistical validity are maintained.

By examining the historical and doctrinal response of copyright law to new technologies, this Article offers a new analytical framework for determining liability for what it terms artificial infringement, or infringing outputs created by generative AI systems. Time and again, new technologies have posed challenges to existing copyright law, straining its capacity to balance protecting authors’ rights to incentivize new expression and providing public access to their works. Courts and Congress have been able to maintain this balance by using a variety of doctrinal tools, including fair use, compulsory licensing, and secondary liability. One underexamined tool, however, is the refinement of direct liability. This Article reveals how courts introduced the causation requirement to maintain copyright’s balance in response to complex machine-generated infringements.

Together, direct liability’s causation requirement and other doctrinal tools provide a viable framework for maintaining copyright’s incentives-access balance despite the acute challenges of artificial infringement. By holding the AI system directly liable, courts can utilize and refine secondary liability doctrines to conduct a more nuanced analysis of user and developer liability for AI-generated infringements. Along with fair use, these refinements to copyright doctrine provide a more comprehensive resolution to the battles between copyright and AI.

Government investigatory and surveillance powers are important tools for examining crime and protecting public safety. However, since these tools must be employed in secret, it can be challenging to identify abuses or changes in use that could be of significant public interest. In this paper, we evaluate this phenomenon in the context of National Security Letters (NSLs). NSLs are a form of legal process that empowers parts of the United States federal government to request certain pieces of information for national security purposes. After initial concerns about the lack of public oversight, Congress worked to increase transparency by mandating government agencies to publish aggregated statistics on the NSL usage and by allowing the private sector to report information on NSLs in transparency reports. The implicit goal is that these transparency mechanisms should deter large-scale abuse by making it visible. We evaluate how well these mechanisms work by carefully analyzing the full range of publicly available data related to NSL use. Our findings suggest that they may not lead to the desired public scrutiny as we find published information requires significant manual effort to collect and parse data due to the lack of structure and context. Moreover, we discovered mistakes (subsequently fixed after our reporting to the ODNI), which suggests a lack of active auditing. Taken together, our case study of NSLs provides insights and suggestions for the successful construction of transparency mechanisms that enable effective public auditing.

Websites’ privacy policies and terms of service constitute valuable resources for scholars in various disciplines. Nonetheless, there exists no large, multilingual database collecting these documents over the long term. Therefore, researchers spend a lot of valuable time collecting them for individual projects, and these heterogeneous methods impede the reproducibility and comparability of research findings. As a solution, we introduce a long-term scraper of privacy policies and terms supporting 37 languages. We run our scraper on a monthly basis on 800 000 websites, and we publish the dataset for the twelve crawls in 2024. Our manual evaluation of the end-to-end extraction of the documents demonstrates F1 scores of 79% for privacy policies and 75% for terms of service in five sample languages (English, German, French, Italian, and Croatian). We present several broad potential applications of our database for future research.

To meet its dual burdens of providing useful statistics and ensuring privacy of individual respondents, the US Census Bureau has for decades introduced some form of “noise” into published statistics. Initially, they used a method known as “swapping” (1990-2010). In 2020, they switched to an algorithm called TopDown that ensures a form of Differential Privacy. While the TopDown algorithm has been made public, no implementation of swapping has been released and many details of the deployed swapping methodology deployed have been kept secret. Further, the Bureau has not published (even a synthetic) “original” dataset and its swapped version. It is therefore difficult to evaluate the effects of swapping, and to compare these effects to those of other privacy technologies. To address these difficulties we describe and implement a parameterized swapping algorithm based on Census publications, court documents, and informal interviews with Census employees. With this implementation, we characterize the impacts of swapping on a range of statistical quantities of interest. We provide intuition for the types of shifts induced by swapping and compare against those introduced by TopDown. We find that even when swapping and TopDown introduce errors of similar magnitude, the direction in which statistics are biased need not be the same across the two techniques. More broadly, our implementation provides researchers with the tools to analyze and potentially correct for the impacts of disclosure avoidance systems on the quantities they study.

On June 8, 2023, a US Supreme Court ruling preliminarily enjoined Alabama from implementing its recent congressional district plan. The court deemed the plan to be in violation of Section 2 of the Voting Rights Act, which prohibits discriminatory election laws against minority communities. This ruling is related to a century-old problem known as gerrymandering, for which many have sought solutions with the help of mathematics. One key remedy to gerrymandering has been the Markov Chain Monte-Carlo (MCMC) redistricting algorithm, widely employed in partisan gerrymandering litigations to detect outliers in district plans. This study introduces a novel MCMC redistricting algorithm for outlier analysis specific to racial gerrymandering. Our innovative approach, the Partial Map MCMC algorithm, utilizes a subset of districts for plan generation while keeping others static. Testing Alabama’s congressional district plan against a standard MCMC algorithm designed for partisan gerrymandering reveals significant differences. Both algorithms were evaluated based on their success in generating district plans with a minimum of two majority-minority districts while maintaining all other constraints constant. Results indicate that the Partial Map MCMC algorithm produces 90% of plans with two majority-minority districts, in stark contrast to the standard MCMC algorithm’s less than 1%. This outcome underscores the suitability of our algorithm in generating a diverse set of district plans for outlier analysis in racial gerrymandering litigations. In conclusion, our research represents a crucial step towards addressing the nuanced challenges of gerrymandering, emphasizing the importance of tailored algorithms to promote equitable representation and safeguard the principles of the Voting Rights Act.

The paper addresses the challenge of ensuring that increasingly powerful autonomous maritime vessels operate safely and conform to regulatory standards. We presents Legata, a domain language designed to ensure regulatory compliance in autonomous maritime vessels. By leveraging large-scale simulations, Legata translates legal regulations into computable terms, enabling precise evaluation of vessel behavior across diverse scenarios. The framework quantifies risk based on regulatory violations, providing a structured method for assessing compliance. A case study on the Istanbul Strait demonstrates Legata’s practical application.

We study the regulation of algorithmic (non-)collusion amongst sellers in dynamic imperfect price competition by auditing their data as introduced by Hartline et al. [23].

We develop an auditing method that tests whether a seller’s pessimistic calibrated regret is low. The pessimistic calibrated regret is the highest calibrated regret of outcomes compatible with the observed data. This method relaxes the previous requirement that a pricing algorithm must use fully-supported price distributions to be auditable. This method is at least as permissive as any auditing method that has a high probability of failing algorithmic outcomes with non-vanishing calibrated regret. Additionally, we strengthen the justification for using vanishing calibrated regret, versus vanishing best-in-hindsight regret, as the non-collusion definition, by showing that even without any side information, the pricing algorithms that only satisfy weaker vanishing best-in-hindsight regret allow an opponent to manipulate them into posting supra-competitive prices. This manipulation cannot be excluded with a non-collusion definition of vanishing best-in-hindsight regret.

We motivate and interpret the approach of auditing algorithms from their data as suggesting a per se rule. However, we demonstrate that it is possible for algorithms to pass the audit by pretending to have higher costs than they actually do. For such scenarios, the rule of reason can be applied to bound the range of costs to those that are reasonable for the domain.

For decades, scholars debated the merits between resolving disputes by public adjudications or private settlements. This tension is particularly relevant in misconduct settlements, where wrongdoers can hide behind the confidentiality available in a private settlement. A paradigmatic example of this was the #MeToo movement and the revelation of serial sexual predators sheltered by secret settlements. Using Multi-Party Computation, a cryptographic technique that enables parties to provide private data for computation without giving up confidentiality, we contribute a fully-interwoven statutory-technological system that implements secure reporting of wrongful misconduct settlements, in order to provide oversight statistics to policymakers and to unmask repeatedly-settling parties for investigation. By providing a unique policy option that balances privacy with oversight, our proposal lessens the need to restrict settlement confidentiality, thereby protecting the autonomy of the parties to settle privately, if they so choose. More broadly, our proposal addresses the oversight complaints against settlements by scholars, and advances the discourse on the appropriate roles of adjudication and settlements in resolving disputes.

Disparate impact doctrine offers an important legal apparatus for targeting discriminatory data-driven algorithmic decisions. A recent body of work has focused on conceptualizing one particular construct from this doctrine: the less discriminatory alternative, an alternative policy that reduces disparities while meeting the same business needs of a status quo or baseline policy. However, attempts to operationalize this construct in the algorithmic setting must grapple with some thorny challenges and ambiguities. In this paper, we attempt raise and resolve important questions about less discriminatory algorithms (LDAs). How should we formally define LDAs, and how does this interact with different societal goals they might serve? And how feasible is it for firms or plaintiffs to computationally search for candidate LDAs? We find that formal LDA definitions face fundamental challenges when they attempt to evaluate and compare predictive models in the absence of held-out data. As a result, we argue that LDA definitions cannot be purely quantitative, and must rely on standards of “reasonableness.” We then raise both mathematical and computational constraints on firms’ ability to efficiently conduct a proactive search for LDAs, but we provide evidence that these limits are “weak” in a formal sense. By defining LDAs formally, we put forward a framework in which both firms and plaintiffs can search for alternative models that comport with societal goals.

When does a digital image resemble reality? The relevance of this question increases as the generation of synthetic images—so-called deep fakes—becomes increasingly popular. Deep fakes have gained much attention for a number of reasons—among others, due to their potential to disrupt the political climate. In order to mitigate these threats, the EU AI Act implements specific transparency regulations for generating synthetic content or manipulating existing content. However, the distinction between real and synthetic images is— even from a computer vision perspective—far from trivial. We argue that the current definition of deep fakes in the AI Act and the corresponding obligations are not sufficiently specified to tackle the challenges posed by deep fakes. By analyzing the life cycle of a digital photo from the camera sensor to the digital editing features, we find that: (1.) Deep fakes are ill-defined in the EU AI Act. The definition leaves too much scope for what a deep fake is. (2.) It is unclear how editing functions like Google’s “best take” feature can be considered as an exception to transparency obligations. (3.) The exception for substantially edited images raises questions about what constitutes substantial editing of content and whether or not this editing must be perceptible by a natural person.

Our results demonstrate that complying with the current AI Act transparency obligations is difficult for providers and deployers. As a consequence of the unclear provisions, there is a risk that exceptions may be either too broad or too limited. We intend our analysis to foster the discussion on what constitutes a deep fake and to raise awareness about the pitfalls in the current AI Act transparency obligations.

Over its decades long history, the field of AI and Law has made significant progress developing and researching formal models of case based reasoning that are capable of producing legal arguments. These models employ argument schemes to replicate legal argumentation. Although their arguments are accurate and explainable, these systems are costly to produce and maintain, requiring manual case representations and expert-crafted algorithms that mimic argument. To address these limitations we employ a prompt-engineering strategy that leads state-of-the-art LLMs to follow argument schemes. We show that it is feasible for LLMs to produce basic case-based legal arguments.

As the legal community increasingly examines the use of large language models (LLMs) for various legal applications, legal AI developers have turned to retrieval-augmented LLMs (“RAG” systems) to improve system performance and robustness. An obstacle to the development of specialized RAG systems is the lack of realistic legal RAG benchmarks which capture the complexity of both legal retrieval and downstream legal question-answering. To address this, we introduce two novel legal RAG benchmarks: Bar Exam QA and Housing Statute QA. Our tasks correspond to real-world legal research tasks, and were produced through annotation processes which resemble legal research. We describe the construction of these benchmarks and the performance of existing retriever pipelines. Our results suggest that legal RAG remains a challenging application, thus motivating future research.

Large language models struggle to answer legal questions that require applying detailed, jurisdiction-specific legal rules. Lawyers also find these types of question difficult to answer. For help, lawyers turn to legal practice guides: expert-written how-to manuals for practicing a type of law in a particular jurisdiction. Might large language models also benefit from consulting these practice guides? This article investigates whether providing LLMs with excerpts from these guides can improve their ability to answer legal questions. Our findings show that adding practice guide excerpts to LLMs’ prompts tends to help LLMs answer legal questions. But even when a practice guide provides clear instructions on how to apply the law, LLMs often fail to correctly answer straightforward legal questions – questions that any lawyer would be expected to answer correctly if given the same information. Performance varies considerably and unpredictably across different language models and legal subject areas. Across our experiments’ different legal domains, no single model consistently outperformed others. LLMs sometimes performed better when a legal question was broken down into separate subquestions for the model to answer over multiple prompts and responses. But sometimes breaking legal questions down resulted in much worse performance. These results suggest that retrieval augmented generation (RAG) will not be enough to overcome LLMs’ shortcomings with applying detailed, jurisdiction-specific legal rules. Replicating our experiments on the recently released OpenAI o1 and o3-mini advanced reasoning models did not result in consistent performance improvements. These findings cast doubt on claims that LLMs will develop competency at legal reasoning tasks without dedicated effort directed toward this specific goal.