Purpose The purpose of this paper is to identify the key roles of transparency in making artificial intelligence (AI) greener (i.e. causing lesser carbon dioxide emissions) during the design, development and manufacturing stages or processes of AI technologies (e.g. apps, systems, agents, tools, artifacts) and use the “explicability requirement” as an essential value within the framework of transparency in supporting arguments for realizing greener AI. Design/methodology/approach The approach of this paper is argumentative, which is supported by ideas from existing literature (...) and documents. Findings This paper puts forward a relevant recommendation for achieving better and sustainable outcomes after the reexamination of the identified roles played by transparency within the AI technology context. The proposed recommendation is based on scientific opinion, which is justified by the roles and importance of the two approaches (compliance and integrity) in ethics management and other areas of ethical studies. Originality/value The originality of this paper falls within the boundary of filling the gap that exists in sustainable AI technology and the roles of transparency. (shrink)

The application of generative artificial intelligence (GenAI) technologies in the field of propaganda influences information creation, dissemination, and reception, and introduces new ethical challenges. This paper revisits the philosophical discourses of Jacques Ellul on technology and propaganda, placing them within the context of the rise of today’s generative AI technologies. Ellul identified the First Industrial Revolution as the initial juncture in the history of human technology that formed technique as a social phenomenon, which subsequently shaped the nature of propaganda as (...) a technique. Subsequent developments in computer technology in the latter half of the 20th century enabled the formation of a technological system. This raises the question: Could generative AI represent another pivotal moment in the evolution of the technological system, and what are the ethical implications of propaganda technology in this context? This article seeks to illuminate current discussions on GenAI technology and propaganda ethics with Ellul’s insightful theoretical insights. In terms of research methodology, this study relies on textual interpretation and classical hermeneutics, including three processes: syntactic text interpretation, historical background, and situational application. Ellul’s research delves into the intrinsic links and inherent ethical dimensions between propaganda and technology, examining their comprehensive and enduring impacts. This normative perspective is crucial for a deep understanding of contemporary propaganda within the framework of emerging technologies, helping us to transcend the escalating spiral of propaganda technology and counter-propaganda techniques. By incorporating propaganda facilitated by generative AI technologies into the overall development logic of the technological society, this approach explores its ethical implications from a more macroscopic and holistic perspective. (shrink)

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. Here, we propose a novel way of tackling the value alignment problem as a two-step process. The first step consists on formalising moral values and value aligned behaviour based on philosophical foundations. Our formalisation is compatible with the framework of (Multi-Objective) Reinforcement Learning, to ease the handling of an agent’s individual and ethical objectives. The second step consists in designing an (...) environment wherein an agent learns to behave ethically while pursuing its individual objective. We leverage on our theoretical results to introduce an algorithm that automates our two-step approach. In the cases where value-aligned behaviour is possible, our algorithm produces a learning environment for the agent wherein it will learn a value-aligned behaviour. (shrink)

Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems and proposed technical solutions to address these, we argue that effective solutions require human engagement. Furthermore, there is a lack of research on how to motivate the adoption of these solutions and promote investment in designing AI systems that align with values (...) such as transparency and fairness from the outset. Drawing on insights from psychological theories, we assert the need to understand the values that underlie decisions made by individuals involved in creating and deploying AI systems. We describe how this understanding can be leveraged to increase engagement with de-biasing and fairness-enhancing practices within the AI healthcare industry, ultimately leading to sustained behavioral change via autonomy-supportive communication strategies rooted in motivational and social psychology theories. In developing these pathways to engagement, we consider the norms and needs that govern the AI healthcare domain, and we evaluate incentives for maintaining the status quo against economic, legal, and social incentives for behavior change in line with transparency and fairness values. (shrink)