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Large Language Models (LLMs) hold promise for advancing legal practice by automating complex tasks and improving access to justice. However, their adoption is limited by concerns over client confidentiality, especially when lawyers include sensitive Personally Identifiable Information (PII) in prompts, risking unauthorized data exposure. To mitigate this, we introduce LegalGuardian, a lightweight, privacy-preserving framework tailored for lawyers using LLM-based tools. LegalGuardian employs Named Entity Recognition (NER) techniques and local LLMs to mask and unmask confidential PII within prompts, safeguarding sensitive data before any external interaction. We detail its development and assess its effectiveness using a synthetic prompt library in immigration law scenarios. Comparing traditional NER models with one-shot prompted local LLM, we find that LegalGuardian achieves a F1-score of 93% with GLiNER and 97% with Qwen2.5-14B in PII detection. Semantic similarity analysis confirms that the framework maintains high fidelity in outputs, ensuring robust utility of LLM-based tools. Our findings indicate that legal professionals can harness advanced AI technologies without compromising client confidentiality or the quality of legal documents., Comment: 10 pages, 3 figures

As foundation models have accumulated hundreds of millions of users, developers have begun to take steps to prevent harmful types of uses. One salient intervention that foundation model developers adopt is acceptable use policies: legally binding policies that prohibit users from using a model for specific purposes. This paper identifies acceptable use policies from 30 foundation model developers, analyzes the use restrictions they contain, and argues that acceptable use policies are an important lens for understanding the regulation of foundation models. Taken together, developers' acceptable use policies include 127 distinct use restrictions; the wide variety in the number and type of use restrictions may create fragmentation across the AI supply chain. Developers also employ acceptable use policies to prevent competitors or specific industries from making use of their models. Developers alone decide what constitutes acceptable use, and rarely provide transparency about how they enforce their policies. In practice, acceptable use policies are difficult to enforce, and scrupulous enforcement can act as a barrier to researcher access and limit beneficial uses of foundation models. Nevertheless, acceptable use policies for foundation models are an early example of self-regulation that have a significant impact on the market for foundation models and the overall AI ecosystem., Comment: 10 pages, 2 figures, 2 tables

Recent advances in deep learning have promoted the advent of many computational systems capable of performing intelligent actions that, until then, were restricted to the human intellect. In the particular case of human languages, these advances allowed the introduction of applications like ChatGPT that are capable of generating coherent text without being explicitly programmed to do so. Instead, these models use large volumes of textual data to learn meaningful representations of human languages. Associated with these advances, concerns about copyright and data privacy infringements caused by these applications have emerged. Despite these concerns, the pace at which new natural language processing applications continued to be developed largely outperformed the introduction of new regulations. Today, communication barriers between legal experts and computer scientists motivate many unintentional legal infringements during the development of such applications. In this paper, a multidisciplinary team intends to bridge this communication gap and promote more compliant Portuguese NLP research by presenting a series of everyday NLP use cases, while highlighting the Portuguese legislation that may arise during its development., Comment: LEGAL2024 Legal and Ethical Issues in Human Language Technologies, LREC 2024

Privacy engineering is gaining momentum in industry and academia alike. So far, manifold low-level primitives and higher-level methods and strategies have successfully been established. Still, fostering adoption in real-world information systems calls for additional aspects to be consciously considered in research and practice.

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI., Comment: This book has been accepted by Springer Nature and will be published as an open access monograph. https://link.springer.com/book/9783031231896. It is licensed under the CC BY-NC-SA license (https://creativecommons.org/licenses/by-nc-sa/4.0/), except for the material included from other authors, which may have different licenses

Natural language processing techniques have helped domain experts solve legal problems. Digital availability of court documents increases possibilities for researchers, who can access them as a source for building datasets -- whose disclosure is aligned with good reproducibility practices in computational research. Large and digitized court systems, such as the Brazilian one, are prone to be explored in that sense. However, personal data protection laws impose restrictions on data exposure and state principles about which researchers should be mindful. Special caution must be taken in cases with human rights violations, such as gender discrimination, over which we elaborate as an example of interest. We present legal and ethical considerations on the issue, as well as guidelines for researchers dealing with this kind of data and deciding whether to disclose it., Comment: 10 pages, 2 figures. To be published in the 4th Workshop on Natural Legal Language Processing (NLLP 2022), co-located with the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)

In recent years, discussions about fairness in machine learning, AI ethics and algorithm audits have increased. Many entities have developed framework guidance to establish a baseline rubric for fairness and accountability. However, in spite of increased discussions and multiple frameworks, algorithm and data auditing still remain difficult to execute in practice. Many open-source auditing tools are available, but users aren't always aware of the tools, what they are useful for, or how to access them. Model auditing and evaluation are not frequently emphasized skills in machine learning. There are also legal reasons for the proactive adoption of these tools that extend beyond the desire for greater fairness in machine learning. There are positive social issues of public perception and goodwill that matter in our highly connected global society. Greater awareness of these tools and the reasons for actively utilizing them may be helpful to the entire continuum of programmers, data scientists, engineers, researchers, users and consumers of AI and machine learning products. It is important for everyone to better understand the input and output differentials, how they are occurring, and what can be done to promote FATE (fairness, accountability, transparency, and ethics) in machine- and deep learning. The ability to freely access open-source auditing tools removes barriers to fairness assessment at the most basic levels of machine learning. This paper aims to reinforce the urgent need to actually use these tools and provides motivations for doing so. The exemplary tools highlighted herein are open-source with software or code-base repositories available that can be used immediately by anyone worldwide.

An important part of law and regulation is demanding explanations for actual and potential failures. We ask questions like: What happened (or might happen) to cause this failure? And why did (or might) it happen? These are disguised normative questions - they really ask what ought to have happened, and how the humans involved ought to have behaved. To answer the normative questions, law and regulation seeks a narrative explanation, a story. At present, we seek these kinds of narrative explanation from AI technology, because as humans we seek to understand technology's working through constructing a story to explain it. Our cultural history makes this inevitable - authors like Asimov, writing narratives about future AI technologies like intelligent robots, have told us that they act in ways explainable by the narrative logic which we use to explain human actions and so they can also be explained to us in those terms. This is, at least currently, not true. This work argues that we can only solve this problem by working from both sides. Technologists will need to find ways to tell us stories which law and regulation can use. But law and regulation will also need to accept different kinds of narratives, which tell stories about fundamental legal and regulatory concepts like fairness and reasonableness that are different from those we are used to., Comment: 26 pages. Also submitted as a pre-print to SSRN, see http://papers.ssrn.com/sol3/papers.cfm?abstract_id=3970518

Purpose limitation is an important privacy principle to ensure that personal data may only be used for the declared purposes it was originally collected for. Ensuring compliance with respective privacy regulations like the GDPR, which codify purpose limitation as an obligation, consequently, is a major challenge in real-world enterprise systems. Technical solutions under the umbrella of purpose-based access control (PBAC), however, focus mostly on data being held at-rest in databases, while PBAC for communication and publish-subscribe messaging in particular has received only little attention. In this paper, we argue for PBAC to be also applied to data-in-transit and introduce and study a concrete proof-of-concept implementation, which extends a popular MQTT message broker with purpose limitation. On this basis, purpose limitation as a core privacy principle can be addressed in enterprise IoT and message-driven integration architectures that do not focus on databases but event-driven communication and integration instead.

In April 2021, the European Commission proposed a Regulation on Artificial Intelligence, known as the AI Act. We present an overview of the Act and analyse its implications, drawing on scholarship ranging from the study of contemporary AI practices to the structure of EU product safety regimes over the last four decades. Aspects of the AI Act, such as different rules for different risk-levels of AI, make sense. But we also find that some provisions of the Draft AI Act have surprising legal implications, whilst others may be largely ineffective at achieving their stated goals. Several overarching aspects, including the enforcement regime and the risks of maximum harmonisation pre-empting legitimate national AI policy, engender significant concern. These issues should be addressed as a priority in the legislative process., Comment: 16 pages, 1 table

Sharing, downloading, and reusing software is common-place, some of which is carried out legally with open source software. When it is not legal, it is unclear just how many copyright infringements and trade secret violations have taken place: does an infringement count for the artefact as a whole or perhaps for each file of the program? To answer this question, it must first be established whether a program should be considered as an integral whole, a collection, or a mere set of distinct files, and why. We argue that a program is a functional whole, availing of, and combining, arguments from mereology, granularity, modularity, unity, and function to substantiate the claim. The argumentation and answer contributes to the ontology of software artefacts, may assist industry in litigation cases, and demonstrates that the notion of unifying relation is operationalisable. Indirectly, it provides support for continued modular design of artefacts following established engineering practices., Comment: 25 pages, 5 figures, extended technical report of the accepted FOIS 2020 paper

This book discusses the necessity and perhaps urgency for the regulation of algorithms on which new technologies rely; technologies that have the potential to re-shape human societies. From commerce and farming to medical care and education, it is difficult to find any aspect of our lives that will not be affected by these emerging technologies. At the same time, artificial intelligence, deep learning, machine learning, cognitive computing, blockchain, virtual reality and augmented reality, belong to the fields most likely to affect law and, in particular, administrative law. The book examines universally applicable patterns in administrative decisions and judicial rulings. First, similarities and divergence in behavior among the different cases are identified by analyzing parameters ranging from geographical location and administrative decisions to judicial reasoning and legal basis. As it turns out, in several of the cases presented, sources of general law, such as competition or labor law, are invoked as a legal basis, due to the lack of current specialized legislation. This book also investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms and considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms. Lastly, it discusses the involvement of representative institutions in algorithmic regulation., Comment: XXI, 82 pages, 5 figures. Cham: Springer

The criminal justice system is currently ill-equipped to improve outcomes of individuals who cycle in and out of the system with a series of misdemeanor offenses. Often due to constraints of caseload and poor record linkage, prior interactions with an individual may not be considered when an individual comes back into the system, let alone in a proactive manner through the application of diversion programs. The Los Angeles City Attorney's Office recently created a new Recidivism Reduction and Drug Diversion unit (R2D2) tasked with reducing recidivism in this population. Here we describe a collaboration with this new unit as a case study for the incorporation of predictive equity into machine learning based decision making in a resource-constrained setting. The program seeks to improve outcomes by developing individually-tailored social service interventions (i.e., diversions, conditional plea agreements, stayed sentencing, or other favorable case disposition based on appropriate social service linkage rather than traditional sentencing methods) for individuals likely to experience subsequent interactions with the criminal justice system, a time and resource-intensive undertaking that necessitates an ability to focus resources on individuals most likely to be involved in a future case. Seeking to achieve both efficiency (through predictive accuracy) and equity (improving outcomes in traditionally under-served communities and working to mitigate existing disparities in criminal justice outcomes), we discuss the equity outcomes we seek to achieve, describe the corresponding choice of a metric for measuring predictive fairness in this context, and explore a set of options for balancing equity and efficiency when building and selecting machine learning models in an operational public policy setting., Comment: 12 pages, 4 figures, 1 algorithm. The definitive Version of Record will be published in the proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '20), January 27-30, 2020, Barcelona, Spain

The European General Data Protection Regulation asserts data subjects' right to be forgotten, i.e., their right to request that all their personal data be deleted from an organizations' data stores. However, fulfilling such requests in large-scale systems is technically challenging. It requires that organizations keep track of all locations in which an individual's data is stored, be able to access and delete it in a reasonable time frame, and be able to prove that all such data was in fact deleted. In addition, organizations must cope with complexities such as multiple, distributed, and continuously evolving systems of record, complex data retention policies and deletion approval workflows. We present a first design pattern and practical implementation of the right to be forgotten on a large scale in Big Data and cloud environments.

The research question this report addresses is: how, and to what extent, those directly involved with the design, development and employment of a specific black box algorithm can be certain that it is not unlawfully discriminating (directly and/or indirectly) against particular persons with protected characteristics (e.g. gender, race and ethnicity)?, Comment: Technical Report, University of Southampton, March 2016

Interest has been revived in the creation of a "bill of rights" for Internet users. This paper analyzes users' rights into ten broad principles, as a basis for assessing what users regard as important and for comparing different multi-issue Internet policy proposals. Stability of the principles is demonstrated in an experimental survey, which also shows that freedoms of users to participate in the design and coding of platforms appear to be viewed as inessential relative to other rights. An analysis of users' rights frameworks that have emerged over the past twenty years similarly shows that such proposals tend to leave out freedoms related to software platforms, as opposed to user data or public networks. Evaluating policy frameworks in a comparative analysis based on prior principles may help people to see what is missing and what is important as the future of the Internet continues to be debated., Comment: 6th International Conference on Social Informatics (SocInfo 2014), Barcelona, 10-13 November 2014, 16 pages, 3 tables

Availing services provided via the Internet became a widely accepted means in organising one's life. Beside others, eLearning goes with this trend as well. But, while employing Internet service makes life more convenient, at the same time, it raises risks with respect to the protection of the users' privacy. This paper analyses the attitudes of eLearning users towards their privacy by, initially, pointing out terminology and legal issues connected with privacy. Further, the concept and implementation as well as a result analysis of a conducted study is presented, which explores the problem area from different perspectives. The paper will show that eLearning users indeed care for the protection of their personal information when using eLearning services. However, their attitudes and behaviour slightly differ. In conclusion, we provide first approaches of assisting possibilities for users how to resolve the difference of requirements and their actual activities with respect to privacy protection.

The author taught two courses on cryptography, one at Duke University aimed at non-mathematics majors and one at Rose-Hulman Institute of Technology aimed at mathematics and computer science majors. Both tried to incorporate technical and societal aspects of cryptography, with varying emphases. This paper will discuss the strengths and weaknesses of both courses and compare the differences in the author's approach., Comment: 14 pages; to appear in Cryptologia

Physical disabled access is something that most museums consider very seriously. Indeed, there are normally legal requirements to do so. However, online disabled access is still a relatively novel field. Most museums have not yet considered the issues in depth. The Human-Computer Interface for their websites is normally tested with major browsers, but not with specialist browsers or against the relevant accessibility and validation standards. We consider the current state of the art in this area and mention an accessibility survey of some museum websites., Comment: 2 pages, see http://www2003.org/cdrom/papers/poster/p335/p335-bowen.html

Physical disabled access is something that most cultural institutions such as museums consider very seriously. Indeed, there are normally legal requirements to do so. However, online disabled access is still a relatively novel and developing field. Many cultural organizations have not yet considered the issues in depth and web developers are not necessarily experts either. The interface for websites is normally tested with major browsers, but not with specialist software like text to audio converters for the blind or against the relevant accessibility and validation standards. We consider the current state of the art in this area, especially with respect to aspects of particular importance to the access of cultural heritage., Comment: 10 pages, see http://www.museophile.sbu.ac.uk/pub/jpb/eva2003.pdf

The IEEE 802.11b wireless ethernet standart has several serious security flaws. This paper describes this flaws, surveys wireless networks in the Cologne/Bonn area to get an assessment of the security configurations of fielded networks and analizes the legal protections provided to wireless ethernet operators by german law. We conclude that wireless ethernets without additional security measures are not usable for any transmissions which are not meant for a public audience.