This paper aims to explore how insights from the philosophical and social science literature can be incorporated into the definition of resilient infrastructure so that considerations of social justice can be accounted for and addressed more adequately. Building on the view that engineering ultimately aims to promote societal well-being, this paper argues that human well-being depends on (1) the resilience of the physical infrastructure and (2) the socioeconomic context, both of which in turn affect (i) how the impact and recovery of the physical infrastructure translates into societal impact and recovery and (ii) the ability of individuals to recover/adapt independently from the recovery of the physical infrastructure. The paper suggests that a capability approach may be a suitable framework for providing content to the formal concept of resilience as a capability approach is especially suitable for expressing non-tangible damage that might be caused by natural hazards and disasters.

As the use of AI systems continues to increase, so do concerns over their lack of fairness, legitimacy and accountability. Such harmful automated decision-making can be guarded against by ensuring AI systems are contestable by design: responsive to human intervention throughout the system lifecycle. Contestable AI by design is a small but growing field of research. However, most available knowledge requires a significant amount of translation to be applicable in practice. A proven way of conveying intermediate-level, generative design knowledge is in the form of frameworks. In this article we use qualitative-interpretative methods and visual mapping techniques to extract from the literature sociotechnical features and practices that contribute to contestable AI, and synthesize these into a design framework.

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.

Recent years have seen a rise of techniques based on artificial intelligence (AI). With that have also come initiatives for guidance on how to develop “responsible AI” aligned with human and ethical values. Compared to sectors like energy, healthcare, or transportation, the use of AI-based techniques in the water domain is relatively modest. This paper presents a review of current AI applications in the water domain and develops some tentative insights as to what “responsible AI” could mean there. Building on the reviewed literature, four categories of application are identified: modeling, prediction and forecasting, decision support and operational management, and optimization. We also identify three insights pertaining to the water sector in particular: the use of AI techniques in general, and many-objective optimization in particular, that allow for a pluralism of values and changing values; the use of theory-guided data science, which can avoid some of the pitfalls of strictly data-driven models; and the ability to build on experiences with participatory decision-making in the water sector. These insights suggest that the development and application of responsible AI techniques for the water sector should not be left to data scientists alone, but requires concerted effort by water professionals and data scientists working together, complemented with expertise from the social sciences and humanities.

This paper presents a systematic review of the concept of resilience in the field of disaster management, with a focus on the use of indicators and the inclusion of social justice considerations. The literature is reviewed with reference to various definitions of resilience, the relation between concepts of resilience and vulnerability, the conceptualization of resilience and the use of indicators, and the inclusion of social justice issues. The analysis shows that different disciplines employ various definitions of resilience and conceptions of its relation to vulnerability. Although recognized as important, distributive issues are not currently addressed in the literature. As a result, we lack a clear sense of what equality or distributive justice should mean in the context of resilience and disaster management. An approach based on capabilities is proposed as a promising way forward.