Through the exponential growth in digital devices and computational capabilities, big data technologies are putting pressure upon the boundaries of what can or cannot be considered acceptable from an ethical perspective. Much of the literature on ethical issues related to big data and big data technologies focuses on separate values such as privacy, human dignity, justice or autonomy. More holistic approaches, allowing a more comprehensive view and better balancing of values, usually focus on either a design-based approach, in which it (...) is tried to implement values into the design of new technologies, or an application-based approach, in which it is tried to address the ways in which new technologies are used. Some integrated approaches do exist, but typically are more general in nature. This offers a broad scope of application, but may not always be tailored to the specific nature of big data related ethical issues. In this paper we distil a comprehensive set of ethical values from existing design-based and application-based ethical approaches for new technologies and further focus these values to the context of emerging big data technologies. A total of four value lists were selected for this. The integrated list consists of a total of ten values: human welfare, autonomy, non-maleficence, justice, accountability, trustworthiness, privacy, dignity, solidarity and environmental welfare. Together, this set of values provides a comprehensive and in-depth overview of the values that are to be taken into account for emerging big data technologies. (shrink)

A large part of the explainable AI literature focuses on what explanations are in general, what algorithmic explainability is more specifically, and how to code these principles of explainability into AI systems. Much less attention has been devoted to the question of why algorithmic decisions and systems should be explainable and whether there ought to be a right to explanation and why. We therefore explore the normative landscape of the need for AI to be explainable and individuals having a right (...) to such explanation. This exploration is particularly relevant to the medical domain where the (im)possibility of explainable AI is high on both the research and practitioners’ agenda. The dominant intuition overall is that explainability has and should play a key role in the health context. Notwithstanding the strong normative intuition for having a right to explanation, intuitions can be wrong. So, we need more than an appeal to intuitions when it comes to explaining the normative significance of having a right to explanation when being subject to AI-based decision-making. The aim of the paper is therefore to provide an account of what might underlie the normative intuition. We defend the ‘symmetry thesis’ according to which there is no special normative reason to have a right to explanation when ‘machines’ in the broad sense, make decisions, recommend treatment, discover tumors, and so on. Instead, we argue that we have a right to explanation in cases that involve automated processing that significantly affect our core deliberative agency and which we do not understand, because we have a general moral right to explanation when choices are made which significantly affect us but which we do not understand. (shrink)

Governments, companies, and citizens all think trust is important. Especially today, in the networked era, where we make use of all sorts of e-services and increasingly interact and buy online, trust has become a necessary condition for society to thrive. But what do we mean when we talk about trust and how does the rise of the Internet transform the functioning of trust? This books starts off with a thorough conceptual analysis of trust, drawing on insights from - amongst others (...) philosophy and sociology - to sharpen our understanding of the topic. The book explains how the arrival of large systems - such as the internet - has changed the character of trust which today is no longer based on interpersonal interactions but has become completely mediated by technologies. Based on the layered building plan of the Internet itself, a new conceptual lens called 4 Cs is developed to analyse and understand trust in the networked era. The 4Cs refer to the 4 layers which all have to be taken into account to assess trust online, namely: context, code, codification, and curation. The 4Cs bring together the firsthand experiences of the user (context), the sort of technology that is being used (code), the legal implication (codification) and business interests (curation) in order to get a clear picture of the trust issues that may arise. In the final part of the book some real-life cases are discussed (digital hotel keys, Airbnb, online personalization) to illustrate how trust -analysed through the 4 Cs lens- might flourish or be challenged in our current networked era. (shrink)

This article is an update of Latour’s well-known case of the unreturned hotel key. In recent years, the hotel key has been replaced by a keycard and more recently by a digital key that can be downloaded on a smartphone. This article analyses how—with every step in the innovation process—the trust relation of hotel owner and hotel guest is mediated in a distinct way. The networked ontology of the digital key enables the collection of personal information from which the hotel (...) can tailor its services to the wishes of the hotel guests. While this may be in the interest of the guest, it, however, also makes the guest vulnerable as she has only limited control over the data and comes to depend on the conduct of the hotel. The digital key is not merely a key to open a hotel door; it also unlocks the personal information of the guest. (shrink)