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About the Journal

The Journal of Cross-disciplinary Research in Computational Law (CRCL) invites excellence in law, computer science and other relevant disciplines with a focus on two types of ‘legal technologies’: (1) data-driven (e.g. predictive analytics, ‘intelligent’ search) and (2) code-driven (e.g. smart contracts, algorithmic decision-making (ADM), legal expert systems), and (3) their hybrids (e.g. code-driven decision-making based on data-driven research).

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.

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About CRCL ◉ Editorial team ◉ Submissions

Launching of online first articles

The journal kicked off mid-November 2020 with an invited article by Wolfgang Hoffmann-Riem, former Justice of the German Constitutional Court in Karlsruhe, who was part of the Court when it decided the seminal case that established the fundamental right to the guarantee of the confidentiality and integrity of information technology systems. By inviting him to contribute to the opening issue of CRCL we emphasise our close attention to legal practice.

The second online-first article (late November 2020) is authored by Mireille Hildebrandt, founding member of journal's editoral team. Her article concerns the normative alterity that is inherent in the different technologies that articulate legal norms, arguing that the normative affordances of text-based technologies enabled the rise of the rule of law. She contends that the transition to data- and code-driven 'legal' technologies will transform legal practice and thereby the mode of existence of modern positive law.

In the third online-first article (beginning December 2020) Reuben Binns, with a background in both computer science and philosophy, investigates the difference(s) that make(s) a difference in text-driven law as compared to data- and code-driven decision-making systems. This involves issues of procedure (getting to a right answer in the right way), discretion (dealing with the indeterminary inherent in text-driven systems), anticipation (the role of new case law) and a number of other incompatibilities that are all highly relevant when considering the integration of 'legal technologies' into legal practice or legal scholarship. 

The fourth online-first article (half December 2020) argues the crucial importance of delay in law and the rule of law. Whereas efficiency is often associated with speed, Laurence Diver, founding member of this journal’s editorial team, argues that the slowness of a text-driven legal practice may be a feature rather than a bug when it comes to legal protection. To make this point, Diver coined the concept of a ‘hermeneutic gap’ and in this article he explores to what extent ‘slow computing’ might contribute to sustaining this gap before bridging it in the context of legal practice.

In our fifth online-first article (March 2021), Emilie van den Hoven argues that the epistemic opacity that characterises the systems that comprise computational 'law' threatens us with ever-greater 'hermeneutical injustices': constraints on our ability as citizens to make sense of the law, and to account for its place in our lives, which in turn threaten the dignitarian underpinnings of the rule of law.

Data-driven law

Our sixth online first article (November 2021) is the first of a series of articles on the issue of interpretability in machine learning – in the context of data-driven ‘law’. In this article Elena Esposito leaves the well-trodden path of trying to figure out how legal technologies informed by machine learning actually reach their conclusions (output). Instead, she argues that we must learn to interact with these technologies, to use and control them rather than submit to them. Whereas she speaks of ‘communicating with’ and her replier, Federico Cabitzo speaks of ‘relating to’, both emphasise the need to reject objectivist assumptions, while nevertheless actively engaging with them in order to regain the agency that could otherwise be lost in translation.

Our seventh online first article (January 2022) squarely addresses the static temporality that defines machine learning (ML) systems, and its relevance for automated decision support systems that inform legally relevant decisions. ML systems can only be trained on historical data, and the relevance of such data may deteriorate over time, especially in the case of the law, which is a moving target as it adapts to changing circumstances. The author, Sylvie Delacroix, explains how this affects the interpretability and contestability of these systems over time. She proposes to introduce ‘ensemble contestability’ features capable of achieving long-term contestability. Her replier, Zachary Lipton, agrees that the temporal dynamics of ML must be taken into account when considering the impact of ML on the agency of those subject to their decisions. He then moves to succinctly develop three points of critique, arguing, in particular, that Delacroix’s post hoc ‘ensemble contestability’ solution is not viable and cannot do the work that is needed. In the course of his reply, he manages to trace the history of many relevant debates, thus providing a rich resource beyond the usual focus of explainable AI. Delacroix counters by distinguishing between the ‘interpretable object’ and the ‘subject capable of interpretation’, arguing that Lipton’s objections concern the ‘interpretable object’, whereas her concern sides with the subject capable of interpretation. Here we see the need for and the salient results of a genuine, respectful cross-disciplinary conversation.  


  • The Structure and Legal Interpretation of Computer Programs

    James Grimmelmann

    This is an essay about the relationship between legal interpretation and software interpretation, and in particular about what we gain by thinking about computers and programmers as interpreters in the same way that lawyers and judges are interpreters. I wish to propose that there is something to be gained by treating software as another type of law-like text, one that has its own interpretive rules, and that can be analysed using the conceptual tools we typically apply to legal interpretation. In particular, we can usefully distinguish three types of meaning that a program can have. The first is naive functional meaning: the effects that a program has when executed on a specific computer on a specific occasion. The second is literal functional meaning: the effects that a program would have if executed on a correctly functioning computer. The third is ordinary functional meaning: the effects that a program would have if executed correctly and was free of bugs. The punchline is that literal and ordinary functional meaning are inescapably social. The notions of what makes a computer ‘correctly functioning’ and what makes a program ‘bug free’ depend on the conventions of a particular technical community. We cannot reduce the meaning and effects of software to purely technical questions, because although meaning in programming languages is conventional in a different way than meaning in natural languages, it is conventional all the same.

    Reply by Marieke Huisman, University of Twente.

  • Rules, judgment and mechanisation

    Mazviita Chirimuuta

    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.

    Reply by William Lucy, University of Durham.

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