To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
As its name indicates, algorithmic regulation relies on the automation of regulatory processes through algorithms. Examining the impact of algorithmic regulation on the rule of law hence first requires an understanding of how algorithms work. In this chapter, I therefore start by focusing on the technical aspects of algorithmic systems (Section 2.1), and complement this discussion with an overview of their societal impact, emphasising their societal embeddedness and the consequences thereof (Section 2.2). Next, I examine how and why public authorities rely on algorithmic systems to inform and take administrative acts, with special attention to the historical adoption of such systems, and their impact on the role of discretion (Section 2.3). Finally, I draw some conclusions for subsequent chapters (Section 2.4).
In this book, I examined how public authorities’ reliance on algorithmic regulation can affect the rule of law and erode its protective role. I conceptualised this threat as algorithmic rule by law and evaluated the EU legal framework’s safeguards to counter it. In this chapter, I summarise my findings, conclude that this threat is insufficiently addressed (Section 6.1) and provide a number of recommendations (Section 6.2). Finally, I offer some closing remarks (Section 6.3). Algorithmic regulation promises simplicity and a route to avoid the complex tensions of legal rules that are continuously open to multiple interpretations. Yet the same promise also threatens liberal democracy today, as illiberal and authoritarian tendencies seek to eliminate plurality in favour of simplicity. The threat of algorithmic rule by law is hence the same that also threatens liberal democracy: the elimination of normative tensions by essentialising a single view. The antidote is hence to accept not only the normative tensions that are inherent in law but also the tensions inherent in a pluralistic society. We should not essentialise the law’s interpretation, but embrace its normative complexity.
This chapter introduces the main research themes of this book, which explores two current global developments. The first concerns the increased use of algorithmic systems by public authorities in a way that raises significant ethical and legal challenges. The second concerns the erosion of the rule of law and the rise of authoritarian and illiberal tendencies in liberal democracies, including in Europe. While each of these developments is worrying as such, in this book, I argue that the combination of their harms is currently underexamined. By analysing how the former development might reinforce the latter, this book seeks to provide a better understanding of how algorithmic regulation can erode the rule of law and lead to algorithmic rule by law instead. It also evaluates the current EU legal framework which is inadequate to counter this threat, and identifies new pathways forward.
In Chapter 3, I developed this book’s normative analytical framework by concretising the six principles that can be said to constitute the rule of law in the EU legal order. Drawing on this framework, in this chapter I now revisit each of these principles and carry out a systematic assessment of how public authorities’ reliance on algorithmic regulation can adversely affect them (Section 4.1). I then propose a theory of harm that conceptualises this threat, by juxtaposing the rule of law to algorithmic rule by law (Section 4.2). Finally, I summarise my findings and outline the main elements that should be considered when evaluating the aptness of the current legal framework to address this threat (Section 4.3).
Robot pick-and-place for unknown objects is still a very challenging research topic. This paper proposes a multi-modal learning method for robot one-shot imitation of pick-and-place tasks. This method aims to enhance the generality of industrial robots while reducing the amount of data and training costs the one-shot imitation method relies on. The method first categorizes human demonstration videos into different tasks, and these tasks are classified into six types to symbolize as many types of pick-and-place tasks as possible. Second, the method generates multi-modal prompts and finally predicts the action of the robot and completes the symbolic pick-and-place task in industrial production. A carefully curated dataset is created to complement the method. The dataset consists of human demonstration videos and instance images focused on real-world scenes and industrial tasks, which fosters adaptable and efficient learning. Experimental results demonstrate favorable success rates and loss results both in simulation environments and real-world experiments, confirming its effectiveness and practicality.
We suggest that foundation models are general purpose solutions similar to general purpose programmable microprocessors, where fine-tuning and prompt-engineering are analogous to coding for microprocessors. Evaluating general purpose solutions is not like hypothesis testing. We want to know how well the machine will perform on an unknown program with unknown inputs for unknown users with unknown budgets and unknown utility functions. This paper is based on an invited talk by John Mashey, “Lessons from SPEC,” at an ACL-2021 workshop on benchmarking. Mashey started by describing Standard Performance Evaluation Corporation (SPEC), a benchmark that has had more impact than benchmarks in our field because SPEC addresses an import commercial question: which CPU should I buy? In addition, SPEC can be interpreted to show that CPUs are 50,000 faster than they were 40 years ago. It is remarkable that we can make such statements without specifying the program, users, task, dataset, etc. It would be desirable to make quantitative statements about improvements of general purpose foundation models over years/decades without specifying tasks, datasets, use cases, etc.
The risks emanating from algorithmic rule by law lie at the intersection of two regulatory domains: regulation pertaining to the rule of law’s protection (the EU’s rule of law agenda), and regulation pertaining to the protection of individuals against the risks of algorithmic systems (the EU’s digital agenda). Each of these domains consists of a broad range of legislation, including not only primary and secondary EU law, but also soft law. In what follows, I confine my investigation to those areas of legislation that are most relevant for the identified concerns. After addressing the EU’s competences to take legal action in this field (Section 5.1), I respectively examine safeguards provided by regulation pertaining to the rule of law (Section 5.2), to personal data (Section 5.3) and to algorithmic systems (Section 5.4), before concluding (Section 5.5).
In this chapter, I first examine how the rule of law has been defined in legal theory, and how it has been distinguished from the rule by law, which is a distortion thereof (Section 3.1). Second, I assess how the rule of law has been conceptualised in the context of the European Union, as this book focuses primarily on the EU legal order (Section 3.2). In this regard, I also draw on the acquis of the Council of Europe. The Council of Europe is a distinct jurisdictional order, yet it heavily influenced the ‘EU’ conceptualisation of the rule of law, and the EU regularly relies on Council of Europe sources in its own legal practices. Finally, I draw on these findings to identify the rule of law’s core principles and to distil the concrete requirements that public authorities must fulfil to comply therewith (Section 3.3). Identifying these requirements – and the inherent challenges to achieve them – will subsequently allow me to build a normative analytical framework that I can use as a benchmark in Chapter 4 to assess how algorithmic regulation impacts the rule of law.
In numerous applications, extracting a single rotation component (termed “planar rotation”) from a 3D rotation is of significant interest. In biomechanics, for example, the analysis of joint angles within anatomical planes offers better clinical interpretability than spatial rotations. Moreover, in parallel kinematics robotic machines, unwished rotations about an axis – termed “parasitic motions” – need to be excluded. However, due to the non-Abelian nature of spatial rotations, these components cannot be extracted by simple projections as in a vector space. Despite extensive discussion in the literature about the non-uniqueness and distortion of the results due to the nonlinearity of the SO(3) group, they continue to be used due to the absence of alternatives. This paper reviews the existing methods for planar-rotation extraction from 3D rotations, showing their similarities and differences as well as inconsistencies by mathematical analysis as well as two application cases, one of them from biomechanics (flexural knee angle in the sagittal plane). Moreover, a novel, simple, and efficient method based on a pseudo-projection of the Quaternion rotation vector is introduced, which circumvents the ambiguity and distortion problems of existing approaches. In this respect, a novel method for determining the orientation of a box from camera recordings based on a two-plane projection is also proposed, which yields more precise results than the existing Perspective 3-Point Problem from the literature. This paper focuses exclusively on the case of finite rotations, as infinitesimal rotations within a single plane are non-holonomic and, through integration, produce rotation components orthogonal to the plane.
For relevant logics, the admissibility of the rule of proof $\gamma $ has played a significant historical role in the development of relevant logics. For first-order logics, however, there have been only a handful of $\gamma $-admissibility proofs for a select few logics. Here we show that, for each logic L of a wide range of propositional relevant logics for which excluded middle is valid (with fusion and the Ackermann truth constant), the first-order extensions QL and LQ admit $\gamma $. Specifically, these are particular “conventionally normal” extensions of the logic $\mathbf {G}^{g,d}$, which is the least propositional relevant logic (with the usual relational semantics) that admits $\gamma $ by the method of normal models. We also note the circumstances in which our results apply to logics without fusion and the Ackermann truth constant.