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 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.

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 some situations in which undesirable collective effects occur, it is very hard, if not impossible, to hold any individual reasonably responsible. Such a situation may be referred to as the problem of many hands. In this paper we investigate how the problem of many hands can best be understood and why, and when, it exactly constitutes a problem. After analyzing climate change as an example, we propose to define the problem of many hands as the occurrence of a gap in the distribution of responsibility that may be considered morally problematic. Whether a gap is morally problematic, we suggest, depends on the reasons why responsibility is distributed. This, in turn, depends, at least in part, on the sense of responsibility employed, a main distinction being that between backward-looking and forward-looking responsibility.

How are we to appraise new technological developments that may bring revolutionary social changes? Currently this is often done by trying to predict or anticipate social consequences and to use these as a basis for moral and regulatory appraisal. Such an approach can, however, not deal with the uncertainties and unknowns that are inherent in social changes induced by technological development. An alternative approach is proposed that conceives of the introduction of new technologies into society as a social experiment. An ethical framework for the acceptability of such experiments is developed based on the bioethical principles for experiments with human subjects: non-maleficence, beneficence, respect for autonomy, and justice. This provides a handle for the moral and regulatory assessment of new technologies and their impact on society.

In the value sensitive design (VSD) literature, there has been little attention for how values may change during the adoption and use of a sociotechnical system, and what that implies for design. A value change taxonomy is proposed, as well as a number of technical features that allow dealing with value change.