Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. The COVID-19 pandemic, however, has significantly increased the utilisation of health-oriented chatbots, for instance, as a conversational interface to answer questions, recommend care options, check symptoms and complete tasks such as booking appointments. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence (...) clinical practices and expert–client relationships. We suggest the need for new approaches in professional ethics as the large-scale deployment of artificial intelligence may revolutionise professional decision-making and client–expert interaction in healthcare organisations. We argue that the implementation of chatbots amplifies the project of rationality and automation in clinical practice and alters traditional decision-making practices based on epistemic probability and prudence. This article contributes to the discussion on the ethical challenges posed by chatbots from the perspective of healthcare professional ethics. (shrink)

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)

Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data (...) processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM. (shrink)

AI as decision support supposedly helps human agents make ‘better’decisions more efficiently. However, research shows that it can, sometimes greatly, influence the decisions of its human users. While there has been a fair amount of research on intended AI influence, there seem to be great gaps within both theoretical and practical studies concerning unintended AI influence. In this paper I aim to address some of these gaps, and hope to shed some light on the ethical and moral concerns that arise (...) with unintended AI influence. I argue that unintended AI influence has important implications for the way we perceive and evaluate human-AI interaction. To make this point approachable from both the theoretical and practical side, and to avoid anthropocentricallyladen ambiguities, I introduce the notion of decision points. Based on this, the main argument of this paper will be presented in two consecutive steps: i) unintended AI influence doesn’t allow for an appropriate determination of decision points-this will be introduced as decision-point-dilemma, and ii) this has important implications for the ascription of responsibility. (shrink)