The concept of code-driven law, i.e. of "legal norms or policies that have been articulated in computer code" by some actors with normative competence, has been convincingly elaborated by Hildebrandt [1]. Its introduction has the merit to refocus the discussion on the role of artificial devices in the legal activity, rather than on ontological positions expressed under code-is-law or law-is-code banners, which are present, with various interpretations and changing fortunes, in the literature and practice of contemporary regulatory technologies, and technology-oriented legal scholarship (see the overview in [2]). According to Hildebrandt, code-driven law should be distinguished from data-driven law, i.e. computational decision-making derived from statistical or other inductive methods, and from text-driven law, i.e. the legal activity performed by humans by means of sources of norms such as statutory and case law. A crucial difference between these forms of "law" is that the linguistic artifacts used in text-driven law are characterized by open-textured concepts (e.g.

The Manitoba First Nation School System is encouraging teachers to leverage students' love for video games and educate them about traditional teachings via e-sports clubs and classes. Over the last decade, a growing number of school leaders both on and off-reserve have started using online applications such as Minecraft. By forcing e-learning into the mainstream, COVID-19 has made unconventional educational tools even more popular. Not only are video games an engaging way to teach collaboration and digital literacy, said Karl Hildebrandt, but the education technology facilitator at MFNSS said they pair well with foundational Anishinaabe principles on conducting oneself towards others. Video games are an engaging way to teach collaboration and digital literacy.

Some people think they are above the law. In a constitutional democracy this cannot be the case. Neither the head of state nor the doctor or the police are above the law. They should all be enabled to do their work, but we do not buy the claim that they could act as they wish. In 18th century Europe we replaced the authoritarian rule by law with a rule of law, to mitigate uninhibited power, and to ensure that those in power can be held to account in a court of law.

Digital services have frequently been in collision -- if not out-and-out conflict -- with the rule of law. But what happens when technologies such as deep learning software and self-executing code are in the driving seat of legal decisions? How can we be sure next-gen'legal tech' systems are not unfairly biased against certain groups or individuals? And what skills will lawyers need to develop to be able to properly assess the quality of the justice flowing from data-driven decisions? While entrepreneurs have been eyeing traditional legal processes for some years now, with a cost-cutting gleam in their eye and the word'streamline' on their lips, this early phase of legal innovation pales in significance beside the transformative potential of AI technologies that are already pushing their algorithmic fingers into legal processes -- and perhaps shifting the line of the law itself in the process.

This paper takes the perspective of law and philosophy, integrating insights from computer science. First, I will argue that in the era of big data analytics we need an understanding of privacy that is capable of protecting what is uncountable, incalculable or incomputable about individual persons. To instigate this new dimension of the right to privacy I expand previous work on the relational and ecological nature of privacy and the productive indeterminacy of human identity. Second, I will explain that this does not imply a rejection of machine learning, based on a more in-depth study of the assumptions, operations and implications of the practice of machine learning – highlighting its alignment with purpose limitation as core to its methodological integrity. Instead of rejecting machine learning, I advocate a practice of'agonistic machine learning' as core to scientifically viable integration of data-driven applications into our environments while simultaneously bringing them under the Rule of Law. This should also provide the best means to achieve effective protection against overdetermination of individuals by machine inferences.

Joss Wright is training a robot to freak people out. Wright, a computer scientist, is plotting an experiment with a humanoid robot called Nao. He and his colleagues plan to introduce this cute bot to people on the street and elsewhere – where it will deliberately invade their privacy. Upon meeting strangers, for example, Nao may use face-recognition software to dig up some detailed information online about them. Or, it may tap into their mobile phone's location tracking history, learn where they ate lunch yesterday, and ask what they thought of the soup.

Scope This workshop reviewed and classified the various models which have emerged from the general concept of selective attention and context dependency, and sought to identify their commonalities. It was concluded that the motivation and mechanism of these functional models are "efficiency" and ''factoring'', respectively. The workshop focused on computational models of selective attention and context dependency within the realm of neural networks. We treated only ''functional'' models; computational models of biological neural systems, and symbolic or rule-based systems were omitted from the discussion. Presentations Thomas H. Hildebrandt presented the results of his recent survey of the literature on functional models of selective attention and context dependency.

Scope This workshop reviewed and classified the various models which have emerged from the general concept of selective attention and context dependency, and sought to identify their commonalities. It was concluded that the motivation and mechanism of these functional models are "efficiency" and ''factoring'', respectively. The workshop focused on computational models of selective attention and context dependency within the realm of neural networks. We treated only ''functional'' models; computational models of biological neural systems, and symbolic or rule-based systems were omitted from the discussion. Presentations Thomas H. Hildebrandt presented the results of his recent survey of the literature on functional models of selective attention and context dependency.

Scope This workshop reviewed and classified the various models which have emerged from the general concept of selective attention and context dependency, and sought to identify their commonalities. It was concluded that the motivation and mechanism ofthese functional models are "efficiency" and ''factoring'', respectively. The workshop focused on computational models of selective attention and context dependency withinthe realm of neural networks. We treated only ''functional'' models; computational models of biological neural systems, and symbolic or rule-based systems were omitted from the discussion. Presentations Thomas H. Hildebrandt presented the results of his recent survey of the literature onfunctional models of selective attention and context dependency.