Computer science

All Items

• Artificial Intelligence and renegotiation of commercial lease contracts affected by pandemic-related contingencies from Covid-19. The Project A.I.A.Co.

  Maurizio Parton, Marco Angelone, Carlo Metta, Stefania D'Ovidio, Roberta Massarelli, Luca Moscardelli, Gianluca Amato, Cristiano De Nobili

  This paper aims to investigate the possibility of using Artificial Intelligence (AI) to resolve the legal issues raised by the Covid-19 emergency about the fate of contracts with continuous, repeated or deferred performance, as well as, more generally, to deal with exceptional events and contingencies. We first study whether the Italian legal system allows for `maintenance' remedies to face contingencies and to avoid the termination of the (duration) contracts, while ensuring effective protection of the interests of both parties. We then give a complete and technical description of an AI-based predictive framework, aimed at assisting both the Magistrate (during the trial) and the parties themselves (in out-of-court proceedings) in the redetermination of the rent of commercial lease contracts. This framework, called A.I.A.Co. for Artificial Intelligence for contract law Against Covid-19, has been developed under the Italian public grant called Fondo Integrativo Speciale per la Ricerca and - even if the predictive system was initially intended to deal with the very specific problem connected to Covid-19 - the knowledge acquired, the model produced and the research outcomes can be easily transferred to other civil issues (such as, for example, those relating to the determination of the amount of the maintenance or divorce obligation in family law).

• Scoping AI & Law Projects: Wanting It All is Counterproductive

  Denis Merigoux

  The intersection of law and computer science has been dominated for decades by a community that self-identifies with the pursuit of ‘artificial intelligence’. This self-identification is not a coincidence; many AI & Law researchers have expressed their interest in the ideologically-charged idea-utopia of government by machines, and the field of artificial intelligence aligns with the pursuit of all-encompassing systems that could crack the very diverse nature of legal tasks. As a consequence, a lot of theoretical and practical work has been carried in the AI & Law community with the objective of creating logic-based, knowledge-based or machine-learning-based systems that could eventually ‘solve’ any legal task. This ‘want-it-all’ research attitude echoes some of the debates in my home field of formal methods around formalization of programming languages and proofs. Hence, I will argue here that the quest for an unscoped system that does it all is counterproductive for multiple reasons. First, because these systems perform generally poorly on everything rather than being good at one task, and most legal applications have high correctness standards. Second, because it yields artifacts that are very difficult to evaluate in order to build a sound methodology for advancing the field. Third, because it nudges into technological choices that require large infrastructure-building (sometimes on a global scale) before reaping benefits and encouraging adoption. Fourth, because it distracts efforts away from the basic applications of legal technologies that have been neglected by the research community.

• Transdisciplinary research as a way forward in AI & Law

  Floris Bex

  The field of Artificial Intelligence & Law is a community of law and computer science scholars, with a focus on AI applications for the law and law enforcement. Such applications have become the subject of much debate, with techno-pessimists and techno-optimists on either side. What is the role of the (largely techno-optimistic) AI & Law community in this debate, how can we investigate AI for the law without getting caught up in the drama? I will argue for three points: (1) combe research on data-driven systems, such as generative AI, with research on knowledge-based AI; (2) put AI into (legal) practice, working together with courts, the police, law firms and citizens; (3) work together across disciplines, bringing together those who think about how to build AI and those who think about how to govern and regulate it.

• Promises and pitfalls of artificial intelligence for legal applications

  Sayash Kapoor, Peter Henderson, Arvind Narayanan

  Is AI set to redefine the legal profession? We argue that this claim is not supported by the current evidence. We dive into AI's increasingly prevalent roles in three types of legal tasks: information processing; tasks involving creativity, reasoning, or judgment; and predictions about the future. We find that the ease of evaluating legal applications varies greatly across legal tasks, based on the ease of identifying correct answers and the observability of information relevant to the task at hand. Tasks that would lead to the most significant changes to the legal profession are also the ones most prone to overoptimism about AI capabilities, as they are harder to evaluate. We make recommendations for better evaluation and deployment of AI in legal contexts.

• The Future of Computational Law in the Context of the Rule of Law

  Mireille Hildebrandt

  In this position paper, I argue that lawyers must come to terms with the advent of a rich variety of legal technologies and define a series of challenges that the position papers in this special issue aim to identify and address. Before doing so, I address the question of what it means to discuss the future of computational law and how that relates to the Rule of Law. This, in turn, raises the question of whether there could be something like ‘a computational Rule of Law’, or whether that would be a bridge too far because neither the concept nor the practice of Rule of Law lends itself to computation. In that case, how would the integration of computational technologies into legal practice relate to a non-computational Rule of Law? The answer to that question will structure the challenges I see for the uptake of legal technologies, resulting in a research agenda that should enable, guide and restrict the design, deployment and use of legal technologies with an eye to the future of law.

• Rules, Computation and Politics: Scrutinizing Unnoticed Programming Choices in French Housing Benefits

  Denis Merigoux, Marie Alauzen, Lilya Slimani

  The article questions the translation of a particular legal statement, a rule of calculation of social rights, into a computer program, able to activate the rights of the concerned citizens. It does not adopt a theoretical perspective on the logic of law and computing, rather a realistic stance on contemporary welfare states, by studying the case of the calculation of housing benefit in France. Lacking access to CRISTAL, the source code of the calculation, we simulated the code base from the letter of the law and met with the writers of the housing law in the ministries to conduct a critical investigation of the source code. Through these interdisciplinary methods, we identified three types of unnoticed micro-choices made by developers when translating the law: imprecision, simplification and invisibilization. These methods also uncover significant sociological understanding of the ordinary writing of law and code in the administration: the absence of a synoptic point of view on a particular domain of the law, the non-pathological character of errors in published texts, and the prevalence of a frontier of automation in the division of bureaucratic work. These results from the explicitation of programming choices, lead us to plead for a re-specification in the field of legal informatics and a reorientation of the investigations in the field of the philosophy and the sociology of law.

• Technical Countermeasures against Adversarial Attacks on Computational Law

  Dario Henri Haux, Alfred Früh

  Adversarial Attacks, commonly described as deliberately induced perturbations, can lead to incorrect outputs such as misclassifications or false predictions in systems based on forms of artificial intelligence. While these changes are often difficult to detect for a human observer, they can cause false results and have impacts on physical as well as intangible objects. In that way, they represent a key challenge in diverse areas, including — among others — legal fields such as the judicial system, law enforcement and legal tech. While computer science is addressing several approaches to mitigate these risks caused by Adversarial Attacks, the issue has not received much attention in legal scholarship so far. This paper aims to fill this gap, tries to assess the risks of and technical defenses against Adversarial Attacks on AI Systems and provides a first assessment of possible legal countermeasures.

• Analogies and Disanalogies Between Machine-Driven and Human-Driven Legal Judgement

  Reuben Binns

  Are there certain desirable properties from text-driven law, which have parallels in data-driven law? As a preliminary exercise, this article explores a range of analogies and disanalogies between text-driven normativity and its data-driven counterparts. Ultimately, the conclusion is that the analogies are weaker than the disanalogies. But the hope is that, in the process of drawing them, we learn something more about the comparison between text and data-driven normativities and the (im?)possibility of data-driven law.

  Reply by Emily M. Bender, Professor of Computational Linguistics, University of Washington.