Documents Philosophical Transactions of the Royal Society A 9 results

"Given the foreseeable pervasiveness of artificial intelligence (AI) in modern societies, it is legitimate and necessary to ask the question how this new technology must be shaped to support the maintenance and strengthening of constitutional democracy. This paper first describes the four core elements of today's digital power concentration, which need to be seen in cumulation and which, seen together, are both a threat to democracy and to functioning markets. It then recalls the experience with the lawless Internet and the relationship between technology and the law as it has developed in the Internet economy and the experience with GDPR before it moves on to the key question for AI in democracy, namely which of the challenges of AI can be safely and with good conscience left to ethics, and which challenges of AI need to be addressed by rules which are enforceable and encompass the legitimacy of democratic process, thus laws. The paper closes with a call for a new culture of incorporating the principles of democracy, rule of law and human rights by design in AI and a three-level technological impact assessment for new technologies like AI as a practical way forward for this purpose."

"The article discusses the governance of the digital as the new challenge posed by technological innovation. It then introduces a new distinction between soft ethics, which applies after legal compliance with legislation, such as the General Data Protection Regulation in the European Union, and hard ethics, which precedes and contributes to shape legislation. It concludes by developing an analysis of the role of digital ethics with respect to digital regulation and digital governance."

"Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EU's recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance of this area. While the GDPR does apply to the use of models in some limited situations, most of its provisions relate to the governance of personal data, while models have traditionally been seen as intellectual property. We present recent work from the information security literature around ‘model inversion' and ‘membership inference' attacks, which indicates that the process of turning training data into machine-learned systems is not one way, and demonstrate how this could lead some models to be legally classified as personal data. Taking this as a probing experiment, we explore the different rights and obligations this would trigger and their utility, and posit future directions for algorithmic governance and regulation."

"This paper explores the question of ethical governance for robotics and artificial intelligence (AI) systems. We outline a roadmap—which links a number of elements, including ethics, standards, regulation, responsible research and innovation, and public engagement -as a framework to guide ethical governance in robotics and AI. We argue that ethical governance is essential to building public trust in robotics and AI, and conclude by proposing five pillars of good ethical governance."

"Scholars have increasingly discussed the legal status(es) of robots and artificial intelligence (AI) systems over the past three decades; however, the 2017 resolution of the EU parliament on the ‘electronic personhood' of AI robots has reignited and even made current debate ideological. Against this background, the aim of the paper is twofold. First, the intent is to show how often today's discussion on the legal status(es) of AI systems leads to different kinds of misunderstanding that regard both the legal personhood of AI robots and their status as accountable agents establishing rights and obligations in contracts and business law. Second, the paper claims that whether or not the legal status of AI systems as accountable agents in civil––as opposed to criminal––law may make sense is an empirical issue, which should not be ‘politicized'. Rather, a pragmatic approach seems preferable, as shown by methods of competitive federalism and legal experimentation. In the light of the classical distinction between primary rules and secondary rules of the law, examples of competitive federalism and legal experimentation aim to show how the secondary rules of the law can help us understanding what kind of primary rules we may wish for our AI robots."

"The deployment of various forms of AI, most notably of machine learning algorithms, radically transforms many domains of social life. In this paper we focus on the news industry, where different algorithms are used to customize news offerings to increasingly specific audience preferences. While this personalization of news enables media organizations to be more receptive to their audience, it can be questioned whether current deployments of algorithmic news recommenders (ANR) live up to their emancipatory promise. Like in various other domains, people have little knowledge of what personal data is used and how such algorithmic curation comes about, let alone that they have any concrete ways to influence these data-driven processes. Instead of going down the intricate avenue of trying to make ANR more transparent, we explore in this article ways to give people more influence over the information news recommendation algorithms provide by thinking about and enabling possibilities to express voice. After differentiating four ideal typical modalities of expressing voice (alternation, awareness, adjustment and obfuscation) which are illustrated with currently existing empirical examples, we present and argue for algorithmic recommender personae as a way for people to take more control over the algorithms that curate people's news provision."

"Using artificial intelligence (AI) technology to replace human decision-making will inevitably create new risks whose consequences are unforeseeable. This naturally leads to calls for regulation, but I argue that it is too early to attempt a general system of AI regulation. Instead, we should work incrementally within the existing legal and regulatory schemes which allocate responsibility, and therefore liability, to persons. Where AI clearly creates risks which current law and regulation cannot deal with adequately, then new regulation will be needed. But in most cases, the current system can work effectively if the producers of AI technology can provide sufficient transparency in explaining how AI decisions are made. Transparency ex post can often be achieved through retrospective analysis of the technology's operations, and will be sufficient if the main goal is to compensate victims of incorrect decisions. Ex ante transparency is more challenging, and can limit the use of some AI technologies such as neural networks. It should only be demanded by regulation where the AI presents risks to fundamental rights, or where society needs reassuring that the technology can safely be used. Masterly inactivity in regulation is likely to achieve a better long-term solution than a rush to regulate in ignorance."

"This paper focuses on consumption in the affluent world and the resulting level, composition and distribution of consumption-based emissions. It argues that public policy should foster the recomposition of consumption, while not disadvantaging poorer groups in the population. To combine these two imperatives entails making a distinction between goods and services that are necessary for a basic level of well-being, and those that are surplus to this requirement. The argument proceeds in six stages. First, the paper outlines a theory of universal need, as an alternative conception of well-being to consumer preference satisfaction. Second, it proposes a dual strategy methodology for identifying need satisfiers or necessities in a given social context. Then, it applies this methodology to identify a minimum bundle of necessary consumption items in the UK and speculates how it might be used to identify a maximum bundle for sustainable consumption. The next part looks at corporate barriers and structural obstacles in the path of sustainable consumption. The following part reveals a further problem: mitigation policies can result in perverse distributional outcomes when operating in contexts of great inequality. The final section suggests four ecosocial public policies that would simultaneously advance sustainable and equitable consumption in rich nations.

This article is part of the themed issue ‘Material demand reduction'."