International Law

Machine learning models, as tools of artificial intelligence, have an increasingly strong potential to become an integral part of judicial decision-making. However, the technical limitations of AI systems—often overlooked by legal scholarship—raise fundamental questions, particularly regarding the preservation of the...

Law, interpretations of law, legal arguments, agreements, etc. are typically expressed in writing, leading to the production of vast corpora of legal text.Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in...

Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A...

Judicial documents and judgments are a rich source of information about legal cases, litigants, and judicial decision-makers. Natural language processing (NLP) based approaches have recently received much attention for their ability to decipher implicit information from text. NLP researchers have...

As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s...

Computational Methods for Legal Analysis Computational analysis can be seen as the most recent innovation in the field of Empirical Legal Studies (ELS). It concerns the use of computer science and big data tools to collect, analyse and understand the...

Almost all law is expressed in natural language; therefore, natural language processing (NLP) is a key component of understanding and predicting law. Natural language processing converts unstructured text into a formal representation that computers can understand and analyze. This technology...

We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning (ML) models are beginning to render some of the high-risk healthcare decisions instead of clinicians, these models pose a novel medicolegal and ethical frontier...

This talk situates the rising field of NLLP in the context of legal scholarship and practice.It will examine how the field relates to existing inquiries in computational law, AI and Law, and computational/empirical legal studies.Similarities, differences, and opportunities for cross-fertilization...

Imagine the amazement that a time traveler from the 1950s would experience from a visit to the present. Our guest might well marvel at: • Instant access to what appears to be all the information in the world accompanied by...

AbstractGenerative artificial intelligence (AI) systems, together with text and data mining (TDM), introduce complex challenges at the junction of data utilization and copyright laws. The inherent reliance of AI on large quantities of data, often encompassing copyrighted materials, results in...

Abstract Artificial Intelligence (‘AI’) technologies present great opportunities for the investment management industry (as well as broader financial services). However, there are presently no regulations specifically aiming at AI in investment management. Does this mean that AI is currently unregulated?...

This study presents a comprehensive and practical framework for auditing algorithmic fairness in financial lending models, addressing the urgent concern of bias in machine-learning systems that increasingly influence credit decisions. As financial institutions shift toward automated underwriting and risk scoring,...

Abstract In the debate on copyright exceptions permitting text and data mining (“TDM”) for the development of generative AI systems, the so-called “three-step test” has become a centre of gravity. The test serves as a universal yardstick for assessing the...

Abstract The advancement of machine learning and artificial intelligence (AI) technology has fundamentally altered the production and ownership of works, including video games. That is because, with the development of AI systems, machines are now capable of not only producing...

Algorithmic decision-making systems have become pervasive across critical domains including employment, housing, healthcare, and criminal justice. While these systems promise enhanced efficiency and objectivity, they increasingly demonstrate patterns of discrimination that perpetuate and amplify existing societal biases. This paper examines...

The integration of Artificial Intelligence (AI) in recruitment processes has revolutionized hiring by increasing efficiency, reducing time-to-hire, and enabling data-driven decision-making. However, despite these advancements, concerns about algorithmic bias and fairness remain central to ethical AI deployment. This paper explores...

Cloud-based machine learning systems are increasingly used in sectors such as healthcare, finance, and public services, where they influence decisions with significant social consequences. While these technologies offer scalability and efficiency, they raise significant concerns regarding security, privacy, and compliance....

Machine learning fairness enhancement methods based on data bias correction are usually divided into two processes: The determination of sensitive attributes (such as race and gender) and the correction of data bias. In terms of determining sensitive attributes, existing studies...

The rapid integration of artificial intelligence (AI) into critical decision-making domains—such as healthcare, finance, law enforcement, and hiring—has raised significant ethical concerns regarding bias and fairness. Algorithmic decision-making systems, if not carefully designed and monitored, risk perpetuating and amplifying societal...

PurposeThis study examines the impact of data bias and algorithmic discrimination on individual credit accessibility in China’s financial system. It aims to align financial inclusion and equity goals with statistical fairness conditions by constructing fairness metrics from multiple dimensions. The...