Journal of Cross-disciplinary Research in Computational Law 2022-04-26T11:08:43+02:00 The CRCL Editors Open Journal Systems <p>The <strong><em>Journal of Cross-disciplinary Research in Computational Law</em></strong><strong> (CRCL)</strong> invites excellence in law, computer science and other relevant disciplines with a focus on two types of ‘legal technologies’: (1) <strong>data-driven</strong> (e.g. predictive analytics, ‘intelligent’ search) and (2) <strong>code-driven</strong> (e.g. smart contracts, algorithmic decision-making (ADM), legal expert systems), and (3) their hybrids (e.g. <strong>code-driven decision-making based on data-driven research</strong>).</p> <p>Legal practice is where computational law will be resisted, used or even fostered. CRCL wishes to raise questions as to (1) when the introduction of legal technologies should be resisted and on what grounds, (2) how and under what conditions they can be integrated into the practice of law and legal research and (3) how their integration may inform, erode or enhance legal protection and the rule of law.</p> <p>Please <strong><a href="">subscribe</a></strong> for updates on upcoming articles and issues.</p> <p><a href="">About CRCL</a> ◉ <a href="">Editorial team</a> ◉ <a href="">Submissions</a></p> Rules, judgment and mechanisation 2022-04-26T11:08:43+02:00 Mazviita Chirimuuta <p>This paper is a philosophical exploration of the notion of judgment, a mode of reasoning that has a central role in legal practice as it currently stands. The first part considers the distinction proposed by Kant, and recently explored historically by Lorraine Daston, between the capacity to follow and execute rules and the capacity to determine whether a general rule applies to a particular situation (that is, judgment). This characterisation of judgment is compared with one proposed by Brian Cantwell Smith, as part of an argument that current AI technologies do not have judgment. The second part of the paper asks whether digital computers could in principle have judgment and concludes with a negative answer.</p> <p><strong>Reply by <a href="">William Lucy</a></strong>, University of Durham.</p> 2023-02-09T00:00:00+01:00 Copyright (c) 2023 Mazviita Chirimuuta Evolutionary Interpretation 2021-05-31T10:41:44+02:00 Simon Deakin Christopher Markou <p>We approach the issue of interpretability in artificial intelligence and law through the lens of evolutionary theory. Evolution is understood as a form of blind or mindless ‘direct fitting’, an iterative process through which a system and its environment are mutually constituted and aligned. The core case is natural selection as described in biology but it is not the only one. Legal reasoning can be understood as a step in the ‘direct fitting’ of law, through a cycle of variation, selection and retention, to its social context. Machine learning, insofar as it relies on error correction through <em>backpropagation</em>, is a version of the same process. It may therefore have value for understanding the long-run dynamics of legal and social change. This is distinct, however, from any use it may have in predicting case outcomes. Legal interpretation in the context of the individual or instant case depends upon the generative power of natural language to extrapolate from existing precedents to novel fact situations. This type of prospective or forward-looking reasoning is unlikely to be well captured by machine learning approaches.</p> <p><strong>Reply by <a href="" target="_blank" rel="noopener">Masha Medvedeva</a></strong>, University of Groningen.</p> 2022-07-20T00:00:00+02:00 Copyright (c) 2022 Simon Deakin, Christopher Markou Diachronic interpretability and machine learning systems 2021-03-07T21:58:36+01:00 Sylvie Delacroix <p>If a system is interpretable today, why would it not be as interpretable in five or ten years time? Years of societal transformations can negatively impact the interpretability of some machine learning (ML) systems for two types of reasons. These two types of reasons are rooted in a truism: interpretability requires both an interpretable object and a subject capable of interpretation. This object <em>versus</em> subject perspective ties in with distinct rationales for interpretable systems: generalisability and contestability. On the generalisability front, when it comes to ascertaining whether the accuracy of some ML model holds beyond the training data, a variety of transparency and explainability strategies have been put forward. These strategies can make us blind to the fact that what an ML system has learned may produce helpful insights when deployed in real-life contexts this year yet become useless faced with next year’s socially transformed cohort. On the contestability front, ethically and legally significant practices presuppose the continuous, uncertain (re)articulation of conflicting values. Without our continued drive to call for better ways of doing things, these discursive practices would wither away. To retain such a collective ability calls for a change in the way we articulate interpretability requirements for systems deployed in ethically and legally significant contexts: we need to build systems whose outputs we are capable of contesting today, as well as in five years’ time. This calls for what I call ‘ensemble contestability’ features.</p> <p><strong>Reply by <a href="">Zachary C. Lipton</a></strong>, Carnegie Mellon University.</p> 2022-01-26T00:00:00+01:00 Copyright (c) 2022 Sylvie Delacroix Transparency versus explanation: The role of ambiguity in legal AI 2021-02-22T20:51:34+01:00 Elena Esposito <p>Dealing with opaque machine learning techniques, the crucial question has become the interpretability of the work of algorithms and their results. The paper argues that the shift towards interpretation requires a move from artificial intelligence to an innovative form of artificial communication. In many cases the goal of explanation is not to reveal the procedures of the machines but to communicate with them and obtain relevant and controlled information. As human explanations do not require transparency of neural connections or thought processes, so algorithmic explanations do not have to disclose the operations of the machine but have to produce reformulations that make sense to their interlocutors. This move has important consequences for legal communication, where ambiguity plays a fundamental role. The problem of interpretation in legal arguments, the paper argues, is not that algorithms do not explain enough but that they must explain too much and too precisely, constraining freedom of interpretation and the contestability of legal decisions. The consequence might be a possible limitation of the autonomy of legal communication that underpins the modern rule of law.</p> <p><strong>Reply by <a href="" target="_blank" rel="noopener">Federico Cabitza</a></strong>, University of Milan-Bicocca.</p> 2021-11-10T00:00:00+01:00 Copyright (c) 2021 Elena Esposito Hermeneutical injustice and the computational turn in law 2021-03-23T12:25:53+01:00 Emilie van den Hoven <p>In this paper, I argue that the computational turn in law poses a potential challenge to the legal protections that the rule of law has traditionally afforded us, of a distinctively hermeneutical kind. Computational law brings increased epistemic opacity to the legal system, thereby constraining our ability to understand the law (and ourselves in light of it). Drawing on epistemology and the work of Miranda Fricker, I argue that the notion of ‘hermeneutical injustice’ captures this condition. Hermeneutical injustice refers to the condition where individuals are dispossessed of the conceptual tools needed to make sense of their own experiences, consequently limiting their ability to articulate them. I argue that in the legal context this poses significant challenges to the interpretation, ‘self-application’ and contestation of the law. Given the crucial importance of those concepts to the rule of law and the notion of human dignity that it rests upon, this paper seeks to explicate why the notion of hermeneutical injustice demands our attention in the face of the rapidly expanding scope of computation in our legal systems.</p> <p><strong>Reply by <a href="" target="_blank" rel="noopener">Ben Green</a></strong>, University of Michigan.</p> 2021-03-23T00:00:00+01:00 Copyright (c) 2021 Emilie van den Hoven Computational legalism and the affordance of delay in law 2020-12-18T17:32:08+01:00 Laurence Diver <p>Delay is a central element of law-as-we-know-it: the ability to interpret legal norms and contest their requirements is contingent on the temporal spaces that text affords citizens. As more computational systems are introduced into the legal system, these spaces are threatened with collapse, as the immediacy of ‘computational legalism’ dispenses with the natural ‘slowness’ of text. In order to preserve the nature of legal protection, we need to be clear about where in the legal process such delays play a normative role and to ensure that they are reflected in the affordances of the computational systems that are so introduced. This entails a focus on the design and production of such systems, and the resistance of the ideology of ‘efficiency’ that pervades contemporary development practices.</p> <p><strong>Reply by <a href="" target="_blank" rel="noopener">Ewa Luger</a></strong>, Chancellor's Fellow, University of Edinburgh.</p> 2020-12-18T00:00:00+01:00 Copyright (c) 2020 Laurence Diver Analogies and Disanalogies Between Machine-Driven and Human-Driven Legal Judgement 2022-01-26T15:08:44+01:00 Reuben Binns <p class="western" style="margin-bottom: 0.42cm; line-height: 108%;">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.</p> <p class="western" style="margin-bottom: 0.42cm; line-height: 108%;"><strong>Reply by <a href="" target="_blank" rel="noopener">Emily M. Bender</a></strong>, Professor of Computational Linguistics, University of Washington.</p> 2020-12-10T00:00:00+01:00 Copyright (c) 2020 Reuben Binns The adaptive nature of text-driven law 2022-01-26T15:10:16+01:00 Mireille Hildebrandt <p>This article introduces the concept of ‘technology-driven normativities’, marking the difference between norms, at the generic level, as legitimate expectations that coordinate human interaction, and subsets of norms at specific levels, such as moral or legal norms. The article is focused on the normativity that is generated by text, fleshing out a set of relevant affordances that are crucial for text-driven law and the rule of law. This concerns the ambiguity of natural language, the resulting open texture of legal concepts, the multi-interpretability of legal norms and, finally, the contestability of their application. This leads to an assessment of legal certainty that thrives on the need to interpret, the ability to contest and the concomitant need to decide the applicability and the meaning of relevant legal norms. Legal certainty thus sustains the adaptive nature of legal norms in the face of changing circumstances, which may not be possible for code- or data-driven law. This understanding of legal certainty demonstrates the meaning of legal protection under text-driven law. A proper understanding of the legal protection that is enabled by current positive law (which is text-driven), should inform the assessment of the protection that could be offered by data- or code-driven law, as they will generate other ‘technology-driven normativities’.</p> <p><strong>Reply by <a href="" target="_blank" rel="noopener">Michael Rovatsos</a></strong>, Professor of Artificial Intelligence, University of Edinburgh.</p> 2020-11-26T00:00:00+01:00 Copyright (c) 2020 Mireille Hildebrandt Legal Technology/Computational Law 2021-01-12T11:10:15+01:00 Wolfgang Hoffmann-Riem <p>Although computers and digital technologies have existed for many decades, their capabilities today have changed dramatically. Current buzzwords like Big Data, artificial intelligence, robotics, and blockchain are shorthand for further leaps in development. The digitalisation of communication, which is a disruptive innovation, and the associated digital transformation of the economy, culture, politics, and public and private communication – indeed, probably of virtually every area of life – will cause dramatic social change. It is essential to prepare for the fact that digitalisation will also have a growing impact on the legal system.</p> <p><strong>Reply by <a href="" target="_blank" rel="noopener">Virginia Dignum</a></strong>, Professor at the Department of Computing Science, Umeå University.</p> 2020-11-12T00:00:00+01:00 Copyright (c) 2020 Wolfgang Hoffmann-Riem