While there is a growing effort towards AI for Sustainability (e.g. towards the sustainable development goals) it is time to move beyond that and to address the sustainability of developing and using AI systems. In this paper I propose a definition of Sustainable AI; Sustainable AI is a movement to foster change in the entire lifecycle of AI products (i.e. idea generation, training, re-tuning, implementation, governance) towards greater ecological integrity and social justice. As such, Sustainable AI is focused on more than AI applications; rather, it addresses the whole sociotechnical system of AI. I have suggested here that Sustainable AI is not about how to sustain the development of AI per say but it is about how to develop AI that is compatible with sustaining environmental resources for current and future generations; economic models for societies; and societal values that are fundamental to a given society. I have articulated that the phrase Sustainable AI be understood as having two branches; AI for sustainability and sustainability of AI (e.g. reduction of carbon emissions and computing power). I propose that Sustainable AI take sustainable development at the core of its definition with three accompanying tensions between AI innovation and equitable resource distribution; inter and intra-generational justice; and, between environment, society, and economy. This paper is not meant to engage with each of the three pillars of sustainability (i.e. social, economic, environment), and as such the pillars of sustainable AI. Rather, this paper is meant to inspire the reader, the policy maker, the AI ethicist, the AI developer to connect with the environment—to remember that there are environmental costs to AI. Further, to direct funding towards sustainable methods of AI.

Many industry leaders and academics from the field of machine ethics would have us believe that the inevitability of robots coming to have a larger role in our lives demands that robots be endowed with moral reasoning capabilities. Robots endowed in this way may be referred to as artificial moral agents (AMA). Reasons often given for developing AMAs are: the prevention of harm, the necessity for public trust, the prevention of immoral use, such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. Although some scholars have challenged the very initiative to develop AMAs, what is currently missing from the debate is a closer examination of the reasons offered by machine ethicists to justify the development of AMAs. This closer examination is especially needed because of the amount of funding currently being allocated to the development of AMAs (from funders like Elon Musk) coupled with the amount of attention researchers and industry leaders receive in the media for their efforts in this direction. The stakes in this debate are high because moral robots would make demands on society; answers to a host of pending questions about what counts as an AMA and whether they are morally responsible for their behavior or not. This paper shifts the burden of proof back to the machine ethicists demanding that they give good reasons to build AMAs. The paper argues that until this is done, the development of commercially available AMAs should not proceed further.

This paper argues that the terms ‘Sustainable artificial intelligence (AI)’ in general and ‘Sustainability of AI’ in particular are overused to the extent that they have lost their meaning. The AI for (social) good movement is a manifestation of this trend in which almost any application used in the context of healthcare or agriculture can be classified as AI for good regardless of whether such applications have been evaluated from a broader perspective. In this paper, we aim to create a common understanding of what the ‘AI for Sustainability’ movement ought to mean. We distinguish between two possible AI for Sustainability applications, namely those that fulfill the necessary conditions and those that fulfill the sufficient conditions. The former are purely predictive systems that serve as information providers. The latter are directly involved in an activity that contributes to a sustainability goal. We argue that taking action is a key element in distinguishing between these two application groups, as inaction is the key bottleneck in effectively tackling climate change. Furthermore, we question how effective the use of AI applications can be for sustainability when the systems themselves are inherently unsustainable. Hence, AI for Sustainability should include both an action that contributes to a sustainable end goal as well as an investigation of the sustainability issues of the AI system itself. Following that, Sustainable AI research can be on a gradient: AI in an application domain, AI towards sustainability, and AI for Sustainability.

With the introduction of the concept of Sustainable AI, considerations of the environmental impact of the technology have begun to enter AI ethics discussions. This, Aimee van Wynsberghe suggests, constitutes a new “third wave of AI ethics” which yet needs to be ushered in. In this paper, we ask what is entailed by Sustainable AI that should warrant such special accentuation. Do we find simply run-of-the-mill AI ethics applied to an environmental context? Or does Sustainable AI constitute a true a “game-changer”? We engage in a discussion about what the “waves of AI ethics” ought to mean and the criteria for labelling a wave as such. We argue that the third wave of AI ethics rests on a turn towards a structural approach for uncovering ethical issues on a broader scale, often paired with an analysis of power structures that prevent the uncovering of these issues.

There are two dominant trends in the humanitarian care of 2019: the ‘technologizing of care’ and the centrality of the humanitarian principles. The concern, however, is that these two trends may conflict with one another. Faced with the growing use of drones in the humanitarian space there is need for ethical reflection to understand if this technology undermines humanitarian care. In the humanitarian space, few agree over the value of drone deployment; one school of thought believes drones can provide a utility serving those in need while another believes the large scale deployment of drones will exacerbate the already prevalent issues facing humanitarian aid providers. We suggest in this paper that the strength of the humanitarian principles approach to answer questions of aid provision can be complimented by a technology-facing approach, namely that of robot ethics. We have shown that for humanitarian actors we ought to be concerned with the risks of a loss of contextualization and de-skilling. For the beneficiary, we raise three concerns associated with the threat to the principle of humanity for this group: a loss of dignity by reducing human-to-human interactions; a threat to dignity through a lack of informational transparency; and, a threat to dignity by failing to account for the physiological and behavioral impacts of the drone on human actors. Although we acknowledge the obstacles (and dangers) associated with understanding the physiological and behavioral impacts we insist that the moral acceptability and desirability of drones in humanitarian contexts is dependent on the findings from such studies and that tailored ethical guidelines for drone deployment in humanitarian action be created to reflect the results of such studies.

To date, the majority of work in the fields of human–robot interaction and robot ethics take as the starting point a dyadic interaction between a human and a robot. It is clear, however, that the impacts of robots in health care (understood as ranging from embodied robots and AI to avatars and chatbots) far exceed the individual with whom the robot is interacting. One of the most critical aspects of introducing robots in health care is how such a “bot” will restructure the health care system in a variety of ways: roles of health care staff will change once “bots” are delegated tasks, certain professions may no longer exist (eg, cleaning robots may remove the need for janitorial staff), the education of health care staff will need to include “bot” training, resources will be reallocated to account for the purchasing of “bots”, and the expertise of health care staff will be called into question (eg, when an AI algorithm predicts something that the physician does not). A well-developed care system that includes “bots” of all kinds should predict and balance the ethical impact equally between not only caregivers and receivers, but for the system within which these actors function. This article proposes a model for doing just this, the human–robot–system interaction (HRSI) model that allows for the ethical assessment of “bots” as mediators between a care receiver and a health care system. The HRSI model has important implications for revealing a new set of ethical issues in the introduction of “bots” in health care and in calling for new forms of empirical research to track possible (unintended) consequences related to the rearrangement of roles and responsibilities in the health care system resulting from the integration of health care “bots”.