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.
Reply by Ewa Luger, Chancellor's Fellow, University of Edinburgh.
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.
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’.
Reply by Michael Rovatsos, Professor of Artificial Intelligence, University of Edinburgh.
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.
Reply by Virginia Dignum, Professor at the Department of Computing Science, Umeå University.
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