Organizations developing machine learning-based (ML) technologies face the complex challenge of achieving high predictive performance while respecting the law. This intersection between ML and the law creates new complexities. As ML model behavior is inferred from training data, legal obligations cannot be operationalized in source code directly. Rather, legal obligations require "indirect" operationalization. However, choosing context-appropriate operationalizations presents two compounding challenges: (1) laws often permit multiple valid operationalizations for a given legal obligation-each with varying degrees of legal adequacy; and, (2) each operationalization creates unpredictable trade-offs among the different legal obligations and with predictive performance. Evaluating these trade-offs requires metrics (or heuristics), which are in turn difficult to validate against legal obligations. Current methodologies fail to fully address these interwoven challenges as they either focus on legal compliance for traditional software or on ML model development without adequately considering legal complexities. In response, we introduce a five-stage interdisciplinary framework that integrates legal and ML-technical analysis during ML model development. This framework facilitates designing ML models in a legally aligned way and identifying high-performing models that are legally justifiable. Legal reasoning guides choices for operationalizations and evaluation metrics, while ML experts ensure technical feasibility, performance optimization and an accurate interpretation of metric values. This framework bridges the gap between more conceptual analysis of law and ML models' need for deterministic specifications. We illustrate its application using a case study in the context of anti-money laundering.

Legal cases require careful logical reasoning following the laws, whereas interactions with non- technical users must be in natural language. As an application combining logical reasoning using Prolog and natural language processing using large language models (LLMs), this paper presents a novel approach and system, LogicLease, to automate the analysis of landlord-tenant legal cases in the state of New York. LogicLease determines compliance with relevant legal requirements by analyzing case descriptions and citing all relevant laws. It leverages LLMs for information extraction and Prolog for legal reasoning. By separating information extraction from legal reasoning, LogicLease achieves greater transparency and control over the legal logic applied to each case. We evaluate the accuracy, efficiency, and robustness of LogicLease through a series of tests, achieving 100% accuracy and an average processing time of 2.57 seconds. LogicLease presents advantages over state-of-the-art LLM- based legal analysis systems by providing clear, step-by-step reasoning, citing specific laws, and distinguishing itself by its ability to avoid hallucinations - a common issue in LLMs.

Modern software has been an integral part of everyday activities in many disciplines and application contexts. Introducing intelligent automation by leveraging artificial intelligence (AI) led to break-throughs in many fields. The effectiveness of AI can be attributed to several factors, among which is the increasing availability of data. Regulations such as the general data protection regulation (GDPR) in the European Union (EU) are introduced to ensure the protection of personal data. Software systems that collect, process, or share personal data are subject to compliance with such regulations. Developing compliant software depends heavily on addressing legal requirements stipulated in applicable regulations, a central activity in the requirements engineering (RE) phase of the software development process. RE is concerned with specifying and maintaining requirements of a system-to-be, including legal requirements. Legal agreements which describe the policies organizations implement for processing personal data can provide an additional source to regulations for eliciting legal requirements. In this chapter, we explore a variety of methods for analyzing legal requirements and exemplify them on GDPR. Specifically, we describe possible alternatives for creating machine-analyzable representations from regulations, survey the existing automated means for enabling compliance verification against regulations, and further reflect on the current challenges of legal requirements analysis.

Auditing financial documents is a very tedious and time-consuming process. As of today, it can already be simplified by employing AI-based solutions to recommend relevant text passages from a report for each legal requirement of rigorous accounting standards. However, these methods need to be fine-tuned regularly, and they require abundant annotated data, which is often lacking in industrial environments. Hence, we present ZeroShotALI, a novel recommender system that leverages a state-of-the-art large language model (LLM) in conjunction with a domain-specifically optimized transformer-based text-matching solution. We find that a two-step approach of first retrieving a number of best matching document sections per legal requirement with a custom BERT-based model and second filtering these selections using an LLM yields significant performance improvements over existing approaches.

Businesses increasingly rely on artificial intelligence (AI) systems to make decisions that can significantly affect individual rights, human safety, and critical business operations. But how do these models derive their conclusions? What data do they use? And can we trust the results? Addressing these questions is the essence of "explainability," and getting it right is becoming essential.

Self-driving car market race heats up; S. Korean regulations lag behind: report (Yonhap) South Korea is lagging behind in revising regulations to prepare for the commercialization of autonomous vehicles compared to other major countries such as the US, Germany and Japan, a Seoul-based think tank said Sunday. The market size of autonomous, or self-driving, vehicles is expected to grow from $7.1 billion in 2020 to $1 trillion by 2035, a report by the Korea Economic Research Institute showed. More than half of the newly launched cars to be sold in 2030 are expected to be equipped with level three autonomous driving technology. Level three autonomous driving means that the driver can hand over control to the vehicle, but must be ready to take over when prompted in a limited number of areas such as on the freeway. Autonomy in vehicles is often categorized in six levels from level zero to five according to a system developed by the US-based SAE International.

COCIR welcomes the inception impact assessment by the European Commission on ethical and legal requirements for Artificial Intelligence (AI) and the opportunity to provide feedback. Continuing our engagement in this area, and following the earlier consultation on the AI White Paper, COCIR is pleased to share its experience and expertise on the use of AI within healthcare. COCIR and its members have recently published a comprehensive in-depth analysis of Artificial Intelligence in Medical Device Legislation. The document provides a thorough analysis of the legal requirements applicable to AI-based medical devices. Based on this analysis COCIR sees no need for novel regulatory frameworks for AI-based medical devices, because the requirements of the EU Medical Device Regulation4 (MDR) in combination with provisions of the General Data Protection Regulation (GDPR) are adequate to ensure excellence and trust in AI in line with European values.

The requirements on explainability imposed by European For decisions adopted by public authorities, two stronger laws and their implications for machine learning requirements are studied: motivation obligations for administrations (ML) models are not always clear. In that perspective, and for judges (imposed by European Convention our research (Bibal et al., Forthcoming) analyzes explanation on Human Rights). For administrative decisions, all factual obligations imposed for private and public decisionmaking, and legal grounds on which the decision is based should be and how they can be implemented by machine provided. For judicial decisions, judges have in addition to learning techniques. For decisions adopted by firms or individuals, we mainly The objectives of those explanation requirements are focus on requirements imposed by general European legislation twofold: first, allowing the recipients of a decision to understand applicable to all the sectors of the economy.

You may perhaps have become aware of the General Data Protection Regulation (GDPR). The office of the Irish Data Protection Commissioner, via business briefings and media advertising, is increasingly highlighting this new European Union regulation, which comes into effect on May 25th. The GDPR preamble asserts: "The protection of natural persons in relation to the processing of personal data is a fundamental right." The key theme is that each of us owns our own data. Any company must therefore explicitly request permission to use any of our personal data, explaining why it would like to do so, and for how long.