What is law (and why should we care)? -- Crazy little thing called "law" -- Austin's sanction theory -- Hart and the rule of recognition -- How to do things with plans -- The making of a legal system -- What law is -- Legal reasoning and judicial decision making -- Hard cases -- Theoretical disagreements -- Dworkin and distrust -- The economy of trust -- The interpretation of plans -- The value of legality.

This chapter contains sections titled: Critical Theories of Technology Technology and Democracy Code and Bias Modernity, Premodernity, Alternative Modernity References and Further Reading.

The book titled The Collaborative Economy in Action: European Perspectives is one of the important outcomes of the COST Action CA16121, From Sharing to Caring: Examining the Socio-Technical Aspects of the Collaborative Economy that was active between March 2017 and September 2021. The Action was funded by the European Cooperation in Science and Technology - COST. The main objective of the COST Action Sharing and Caring is the development of a European network of researchers and practitioners interested in investigating the (...) collaborative economy models, platforms, and their socio-technological implications. The network involves scholars, practitioners, communities, and policymakers. The COST Action Sharing and Caring helped to connect research initiatives across Europe and enabled scientists to develop their ideas by collaborating with peers. This collaboration opportunity represented a boost for the participants’ research, careers, and innovation potential. The main aim of this book is to provide a comprehensive overview of the collaborative economy in European countries with a variety of its aspects for a deeper understanding of the phenomenon as a whole. For this reason, in July 2017, an open call for country reports was distributed among the members of the COST Action Sharing and Caring. Representatives of the member countries were invited to produce short country reports covering: definition of the CE; types and models of the CE; key stakeholders involved; as well as legislation and technological tools relevant for the CE. Submitted reports varied in length and regarding the level of detail included, in accordance with how much information was available in each respective country at the time of writing. Editors of the book have compiled these early reports into a summary report, which was intended as a first step in mapping the state of the CE in Europe. The Member Countries Report on the Collaborative Economy, edited by Gaia Mosconi, Agnieszka Lukasiewicz, and Gabriela Avram that was published on the Sharing and Caring website, represented its first synergetic outcome and provided an overview of the CE phenomenon as interpreted and manifested in each of the countries part of the network. Additionally, Sergio Nassare-Aznar, Kosjenka Dumančić, and Giulia Priora compiled a Preliminary Legal Analysis of Country Reports on Cases of Collaborative Economy. In 2018, after undertaking an analysis of the previous reports’ strengths and weaknesses, the book editors issued a call for an updated version of these country reports. Prof. Ann Light advised the editorial team, proposing a new format for country reports and 4000 words limit. The template included: Introduction, Definition, Key Questions, Examples, Illustration, Context, Developments, Issues, Other Major Players, and Relevant Literature. The new template was approved by the Management Committee in October 2018. The task force that had supported the production of the first series of country reports acted as mentors for the team of authors in each country. The final reports arrived at the end of 2018, bringing the total number of submissions to 30. A call for book editors was issued, and a new editorial team was formed by volunteers from the participants of the COST Action: Andrzej Klimczuk, Vida Česnuityte, Cristina Miguel, Santa Mijalche, Gabriela Avram, Bori Simonovits, Bálint Balázs, Kostas Stefanidis, and Rafael Laurenti. The editorial team organized the double-blind reviews of reports and communicated to the authors the requirements for improving their texts. After reviews, the authors submitted updated versions of their country reports providing up-to-date interdisciplinary analysis on the state of the CE in 2019, when the reports were collected. During the final phase, the chapters were again reviewed by the lead editors together with all editorial team members. At the time, the intention was to update these reports again just before the end of the COST Action Sharing and Caring in 2021 and to produce a third edition. However, the COVID-19 pandemic changed these plans. Thus, this final volume was created by 82 scholars-editors and contributors-and consists of reports on 27 countries participating in the COST Action. M4 - Citavi. (shrink)

The study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming (...) in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society. (shrink)

Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that the GDPR will legally mandate a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systems. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the (...) right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive meaningful, but properly limited, information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a ‘right to be informed’. Further, the ambiguity and limited scope of the ‘right not to be subject to automated decision-making’ contained in Article 22 (from which the alleged ‘right to explanation’ stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative and policy steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018. (shrink)

Statutory interpretation involves the reconstruction of the meaning of a legal statement when it cannot be considered as accepted or granted. This phenomenon needs to be considered not only from the legal and linguistic perspective, but also from the argumentative one - which focuses on the strategies for defending a controversial or doubtful viewpoint. This book draws upon linguistics, legal theory, computing, and dialectics to present an argumentation-based approach to statutory interpretation. By translating and summarizing the existing legal interpretative canons (...) into eleven patterns of natural arguments - called argumentation schemes - the authors offer a system of argumentation strategies for developing, defending, assessing, and attacking an interpretation. Illustrated through major cases from both common and civil law, this methodology is summarized in diagrams and maps for application to computer sciences. These visuals help make the structures, strategies, and vulnerabilities of legal reasoning accessible to both legal professionals and laypeople. (shrink)

Legal positivism maintains a distinction between law as it is and law as it ought to be. In other words, for positivists, a law can be legally valid even if it is immoral. H. L. A. Hart hoped to defend legal positivism against natural law. This paper analyses Hart’s criticism of Gustav Radbruch, a natural lawyer, before suggesting that Hart’s account of legal positivism gives rise to a logical problem. It is concluded that this problem leaves logical space for a (...) theory of natural law based on moral authority rather than legal validity. (shrink)

We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that (...) at least some regulatory proposals for explainable AI could end up setting the bar higher than is necessary or indeed helpful. The demands of practical reason require the justification of action to be pitched at the level of practical reason. Decision tools that support or supplant practical reasoning should not be expected to aim higher than this. We cast this desideratum in terms of Daniel Dennett’s theory of the “intentional stance” and argue that since the justification of action for human purposes takes the form of intentional stance explanation, the justification of algorithmic decisions should take the same form. In practice, this means that the sorts of explanations for algorithmic decisions that are analogous to intentional stance explanations should be preferred over ones that aim at the architectural innards of a decision tool. (shrink)

One of the most noticeable trends in recent years has been the increasing reliance of public decision-making processes on algorithms, i.e. computer-programmed step-by-step instructions for taking a given set of inputs and producing an output. The question raised by this article is whether the rise of such algorithmic governance creates problems for the moral or political legitimacy of our public decision-making processes. Ignoring common concerns with data protection and privacy, it is argued that algorithmic governance does pose a significant threat (...) to the legitimacy of such processes. Modelling my argument on Estlund’s threat of epistocracy, I call this the ‘threat of algocracy’. The article clarifies the nature of this threat and addresses two possible solutions. It is argued that neither solution is likely to be successful, at least not without risking many other things we value about social decision-making. The result is a somewhat pessimistic conclusion in which we confront the possibility that we are creating decision-making processes that constrain and limit opportunities for human participation. (shrink)

Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms' actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algorithmic management, we conducted an online experiment using four managerial decisions that required either mechanical or human skills. We manipulated the decision-maker, and measured perceived fairness, trust, and emotional response. With the mechanical (...) tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers' fairness and trustworthiness were attributed to the manager's authority, whereas algorithms' fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms' perceived lack of intuition and subjective judgment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people's lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people's experiences with algorithmic technologies. (shrink)

Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect (...) to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking. (shrink)

Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be (...) good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever. (shrink)

Various due process provisions designed for use by civil servants in administrative decision-making may become redundant when automated decision-making is taken into use in public administration. Problems with mechanisms of good government, responsibility and liability for automated decisions and the rule of law require attention of the law-maker in adapting legal provisions to this new form of decision-making. Although the general data protection regulation of the European Union is important in acknowledging automated decision-making, most of the legal safeguards within administrative (...) due process have to be provided for by the national law-maker. It is suggested that all countries have a need to review their rules of administrative due process with a view to bringing them up to date regarding the requirements of automated decision-making. In whichever way the legislation is framed, the key issues are that persons who develop the algorithm and the code as well as persons who run or deal with the software within public authorities are aware of the preventive safeguards of legality in the context of automated decision-making, not only of the reactive safeguards constituted by the complaint procedures, and that legal mechanisms exist under which these persons can be held accountable and liable for decisions produced by automated decision-making. It is also argued that only rule-based systems of automatized decision-making are compatible with the rule of law and that there is a general interest in preventing a development into a rule of algorithm. (shrink)