van Uffelen Nynke

AI is developing rapidly, as are concerns about the environmental impact of its training and deployment. Studies about the environmental sustainability of AI have begun to emerge in the past five years, stressing the need for critical reflection on the discursive underbelly of this emerging scholarship. For example, how do authors frame the problem of the environmental impact of AI? Are there any ethical reflections accompanying their reporting, and if so, which ethical theories and principles guide normative considerations about the environmental impact of AI? In this study, we conduct a scoping literature review on (1) how authors refer to and frame the problem of the environmental impact of AI systems, (2) who is ascribed responsibility for mitigating said impact, (3) what mitigative measures are proposed, and (4) what normative commitments justify such prescriptive normative statements. Our findings indicate that most literature on the topic is concentrated in computer science, engineering, and natural sciences, and the humanities are mostly absent. This results in a dominance of technofix attitudes towards the problem, and a narrow and limited engagement with ethical principles and theories. As such, we argue for more interdisciplinary work on the environmental sustainability of AI, leading to more diverse solutions and more explicit and pluralistic ethical starting points, grounded, for example, in relational and more-than-human ethics. The findings of this review highlight gaps in the literature and opportunities for developers, social scientists, and AI ethicists for more effective and diverse responses to AI’s environmental impact.

ABSTRACT‘Energy justice’ has become a concern of professionals who are involved in the energy transition. However, many professionals in the energy domain seem to have difficulty understanding this concept, as it does not fit well into their institutional context. We will present a framework for understanding justice that allows energy actors to cope more effectively with energy justice. This framework, which is based on a re-articulation of the three tenets of energy justice, introduces justice both as an evaluation concept and as an organization principle. It further allows energy actors to navigate the normative uncertainties that characterize the energy transition.

Normative assumptions often remain implicit and undebated in technology development. This is unsurprising because many normative ideas seem universal yet are actually particular standards of dominant social groups. As norms pose as universal, they deny legitimacy to alternatives, ignore moral plurality and eliminate the potential for a more just society. Energy technologies, in particular, have been decoupled from ethical reflection for a long time. Although energy justice scholarship aims to fill this gap, its researchers generally focus on the (un)just effects of already developed energy systems. This chapter illustrates how energy justice can be leveraged in the innovation phase to identify implicit and seemingly universal normative assumptions, unlocking a deeply critical potential beyond merely evaluating the consequences of energy technologies. Four workshops were held with energy storage experts to study their energy justice assumptions, whether they acknowledge moral plurality, and how they deal with normative uncertainty. The results show that experts have—and can articulate—specific conceptions of justice about the scale, time, subjects, and principles of justice in relation to energy storage technologies, such as liberal nationalism, anthropocentrism, and utilitarianism. Yet, other defensible normative assumptions circulate in philosophical debates. Some, but not all, normative uncertainties were acknowledged by the participants. When they were, they resolved normative uncertainties by resorting to utilitarian reasonings, dismissing moral questions as subjective, and relying on existing institutional frameworks. The findings stress the importance of a public debate on energy justice, fostering value change in the long run.

When is AI environmentally just? In recent years, increasing attention has been drawn to the environmental impact of AI. Particularly, the development and training of AI systems require significant amounts of energy, water, and raw materials. This raises new ethical questions, such as: when is it (un)justifiable to develop an AI system, considering its environmental impact? This question has been scarcely addressed in the academic literature. To tackle this question, this article draws from the literature on environmental and energy justice. In that field, three categories of justice are distinguished, referring to three elements that can be unjust: the distribution of benefits and burdens (distributive justice), decision-making procedures (procedural justice), and institutionalized social norms (justice as recognition). Based on these distinctions, we outline the criteria for determining the ethical admissibility of AI, given its ecological impact.

Interdisciplinary collaboration is often seen as the approach to deal with wicked problems, which are problems that involve both scientific uncertainties and normative uncertainties, meaning that there is no consensus on the problem definition and the best course of action. One of the reasons for the difficulty in establishing effective interdisciplinary collaboration is that the normative assumptions of academic disciplines are usually left unarticulated. This paper presents four ideal-typical characterisations of the normative paradigms that are maintained by different disciplines. These paradigms can be sketched out as follows: the moral positions that are considered legitimate are ignored (‘moral denialism’); located at the level of the individual (‘aggregated subjectivism’); located at the level of the community (‘moral collectivism’); or found at a transcendental level (‘transcendental realism’). Each of these paradigms brings about its difficulties for dealing with wicked problems. The paper will also present a heuristic framework that guides interdisciplinary research in dealing with normative plurality by aligning the different scales of contextualisation that appear to underlie the four normative paradigms.