The prospect of increasingly autonomous military robots has raised concerns about the obfuscation of human responsibility. This papers argues that whether or not and to what extent human actors are and will be considered to be responsible for the behavior of robotic systems is and will be the outcome of ongoing negotiations between the various human actors involved. These negotiations are about what technologies should do and mean, but they are also about how responsibility should be interpreted and how it (...) can be best assigned or ascribed. The notion of responsibility practices, as the paper shows, provides a conceptual tool to examine these negotiations as well as the interplay between technological development and the ascription of responsibility. To illustrate the dynamics of responsibility practices the paper explores how the introduction of unmanned aerial vehicles has led to (re)negotiations about responsibility practices, focusing particularly on negotiations within the US Armed Forces. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the (...) ‘what’ of AI ethics —rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs. (shrink)

The complexity of industrial reality, the plurality of legitimate perspectives on risks and the role of emotions in decision-making raise important ethical issues in risk management that are usually overlooked in engineering. Using a questionnaire answered by 200 engineering students from a major engineering school in Canada, the purpose of this study was to assess how their training has influenced their perceptions toward these issues. While our results challenge the stereotypical portrait of the engineer, they also suggest that the current (...) engineering education might fail to empower engineers to engage in ethical risk management. We therefore propose an active-learning method to help in this matter. Carried out through workshops with 34 students in chemical engineering, the effectiveness of this method has been evaluated using group interviews and questionnaires. Our results suggest that such an approach is effective, at least in the short run, to motivate students to engage in ethical risk management and to trigger reflectivity on what it means to be an engineer today. (shrink)

Editors’ Overview: Moral Responsibility in Technology and Engineering Content Type Journal Article Category Original Paper Pages 1-11 DOI 10.1007/s11948-011-9285-z Authors Neelke Doorn, Department of Technology, Policy and Management, Delft University of Technology, P.O. Box 5015, 2600 GA Delft, The Netherlands Ibo van de Poel, Department of Technology, Policy and Management, Delft University of Technology, P.O. Box 5015, 2600 GA Delft, The Netherlands Journal Science and Engineering Ethics Online ISSN 1471-5546 Print ISSN 1353-3452 Journal Volume Volume 18 Journal Issue Volume 18, (...) Number 1. (shrink)

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which draws (...) attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or “patients” of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency. (shrink)