Ibo van de Poel

We propose a new approach for tracing value change. Value change may lead to a mismatch between current value priorities in society and the values for which technologies were designed in the past, such as energy technologies based on fossil fuels, which were developed when sustainability was not considered a very important value. Better anticipating value change is essential to avoid a lack of social acceptance and moral acceptability of technologies. While value change can be studied historically and qualitatively, we propose a more quantitative approach that uses large text corpora. It uses probabilistic topic models, which allow us to trace values that are latent. We demonstrate the approach for five types of value change in technology. Our approach is useful for testing hypotheses about value change, such as verifying whether value change has occurred and identifying patterns of value change. The approach can be used to trace value change for various technologies and text corpora, including scientific articles, newspaper articles, and policy documents.

The ongoing energy transition toward more sustainable energy systems implies a change in the values for which such systems are designed. The energy transition however is not just about sustainability but also about values like energy security and affordability, and we witness the emergence of new values like energy justice and energy democracy. How can we understand such value changes and how can or should they affect the design of future energy systems? This introduction to the special section on value change in energy systems introduces the main themes and questions. It discusses different understandings of values and value change, explains why the topic is important and how it can be methodologically studied.

This paper explores the role of moral uncertainty in explaining the morally disruptive character of new technologies. We argue that existing accounts of technomoral change do not fully explain its disruptiveness. This explanatory gap can be bridged by examining the epistemic dimensions of technomoral change, focusing on moral uncertainty and inquiry. To develop this account, we examine three historical cases: the introduction of the early pregnancy test, the contraception pill, and brain death. The resulting account highlights what we call “differential disruption” and provides a resource for fields such as technology assessment, ethics of technology, and responsible innovation.

Governments and companies are increasingly promoting and organizing Responsible Innovation. It is, however, unclear how the seemingly incompatible demands for responsibility, which is associated with care and caution, can be harmonized with demands for innovation, which is associated with risk-taking and speed. We turn to the tradition of virtue ethics and argue that it can be a strong accomplice to Responsible Innovation by focussing on the agential side of innovation. Virtue ethics offers an adequate response to the epistemic and moral complexity in innovation and encourages moral behaviour. We enumerate a number of virtues that people involved in Responsible Innovation would need to cultivate both related to responsibility, such as justice, anticipation, civility and inclusion, and virtues related to innovation, such as courage, dedication, curiosity and creativity. We put forward practical wisdom as a key virtue to regulate relevant virtues and to deal with the tension between responsibility and innovation. Practical wisdom helps an agent to find an appropriate mean in exercising and expressing the other virtues—where the mean is relative to the specific context of action and the role and abilities of the agent.

In this article, we develop an approach for the moral assessment of research and development networks on the basis of the reflective equilibrium approach proposed by Rawls and Daniels. The reflective equilibrium approach aims at coherence between moral judgments, principles, and background theories. We use this approach because it takes seriously the moral judgments of the actors involved in R & D, whereas it also leaves room for critical reflection about these judgments. It is shown that two norms, namely reflective learning and openness and inclusiveness, which are used in the literature on policy and technological networks, contribute to achieving a justified overlapping consensus. We apply the approach to a case study about the development of an innovative sewage treatment technology and show how in this case the two norms are or could be instrumental in achieving a justified overlapping consensus on relevant moral issues.

Engineering ethics and science and technology studies have until now developed as separate enterprises. The authors argue that they can learn a lot from each other. STS insights can help make engineering ethics open the black box of technology and help discern ethical issues in engineering design. Engineering ethics, on the other hand, might help STS to overcome its normative sterility. The contributions in this special issue show in various ways how the gap between STS and engineering ethics might be overcome. In this editorial introduction, the authors discuss the various contributions briefly and delve into the way the various authors conceptualize the engineering design process and the consequences of those conceptualizations for what ethical issues become visible. They also discuss the implications for the responsibility of engineers for technological development.

The authors explore whether the need for ethical reflection on the part of designing engineers is dependent on the type of design process. They use Vincenti's distinction between normal and radical design and different levels of design hierarchy. These two dimensions are coupled with the concept of ill-structured problems, which are problems in which possible solutions cannot be ordered on a scale from better to worse. Design problems are better structured at lower hierarchical levels and in cases of normal design. Better structured design problems require less ethical reflection on the part of designing engineers if such situations are characterized by the existence of generally accepted normative frameworks. Engineers could then deal with moral problems within the bounds of such frameworks and without the need for further reflection. On the basis of a number of empirical cases, the authors explore whether these ideas hold water. They discuss four cases ranging from a high-level radical design process to a low-level normal design process.

This paper explores the role of moral uncertainty in explaining the morally disruptive character of new technologies. We argue that existing accounts of technomoral change do not fully explain its disruptiveness. This explanatory gap can be bridged by examining the epistemic dimensions of technomoral change, focusing on moral uncertainty and inquiry. To develop this account, we examine three historical cases: the introduction of the early pregnancy test, the contraception pill, and brain death. The resulting account highlights what we call “differential disruption” and provides a resource for fields such as technology assessment, ethics of technology, and responsible innovation.

Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and (moral) values that should be adhered to in the design and deployment of artificial intelligence (AI). These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects these values? To help answer this question, I propose an account for determining when an AI system can be said to embody certain values. This account understands embodied values as the result of design activities intended to embed those values in such systems. AI systems are here understood as a special kind of sociotechnical system that, like traditional sociotechnical systems, are composed of technical artifacts, human agents, and institutions but—in addition—contain artificial agents and certain technical norms that regulate interactions between artificial agents and other elements of the system. The specific challenges and opportunities of embedding values in AI systems are discussed, and some lessons for better embedding values in AI systems are drawn.

In this paper, we argue that the characteristics of digital platforms challenge the fundamental assumptions of value sensitive design (VSD). Traditionally, VSD methods assume that we can identify relevant values during the design phase of new technologies. The underlying assumption is that there is onlyepistemic uncertaintyabout which values will be impacted by a technology. VSD methods suggest that one can predict which values will be affected by new technologies by increasing knowledge about how values are interpreted or understood in context. In contrast, digital platforms exhibit a novel form of uncertainty, namely,ontological uncertainty: even with full information and overview, it cannot be foreseen what users or developers will do with digital platforms. Hence, predictions about which values are affected might not hold. In this paper, we suggest expanding VSD methods to account for value dynamism resulting from ontological uncertainty. Our expansions involve (1) extending VSD to the entire lifecycle of a platform, (2) broadening VSD through the addition of reflexivity, i.e. second-order learning about what values to aim at, and (3) adding specific tools of moral sandboxing and moral prototyping to enhance such reflexivity. While we illustrate our approach with a short case study about ride-sharing platforms such as Uber, our approach is relevant for other technologies exhibiting ontological uncertainty as well, such as machine learning, robotics and artificial intelligence.

Value Sensitive Design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: 1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; 2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good; and 3) extending the VSD process to encompass the whole life cycle of an AI technology in order to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

This article presents a conceptual investigation into the value impacts and relations of algorithms in the domain of justice and security. As a conceptual investigation, it represents one step in a value sensitive design based methodology. Here, we explicate and analyse the expression of values of accuracy, privacy, fairness and equality, property and ownership, and accountability and transparency in this context. We find that values are sensitive to disvalue if algorithms are designed, implemented or deployed inappropriately or without sufficient consideration for their value impacts, potentially resulting in problems including discrimination and constrained autonomy. Furthermore, we outline a framework of conceptual relations of values indicated by our analysis, and potential value tensions in their implementation and deployment with a view towards supporting future research, and supporting the value sensitive design of algorithms in justice and security.

When thinking about ethics, technology is often only mentioned as the source of our problems, not as a potential solution to our moral dilemmas. When thinking about technology, ethics is often only mentioned as a constraint on developments, not as a source and spring of innovation. In this paper, we argue that ethics can be the source of technological development rather than just a constraint and technological progress can create moral progress rather than just moral problems. We show this by an analysis of how technology can contribute to the solution of so-called moral overload or moral dilemmas. Such dilemmas typically create a moral residue that is the basis of a second-order principle that tells us to reshape the world so that we can meet all our moral obligations. We can do so, among other things, through guided technological innovation

Proactively including the ethical and societal issues of new technologies could have a positive effect on their acceptance. These issues could be captured in terms of values. In the literature, the values stakeholders deem important for the development of technology have often been identified. However, the relative ranking of these values in relation to each other have not been studied often. The best worst method is proposed as a possible method to determine the weights of values, hence it is used in an evaluative fashion. The applicability of the method is tested by applying it to the case of smart meters, one of the main components of the smart grid. The importance of values is examined for three dimensions of acceptance namely sociopolitical, market, and household acceptance.