It has been claimed that (1) computer professionals should be held responsible for an undisclosed list of “undesirable events” associated with their work and (2) most if not all computer disasters can be avoided by truly understanding responsibility. Commentators of “A Critique of Positive Responsibility in Computing” argue that this is not Donald Gotterbarn’s view (Gotterbarn, JSEE 14(2):235–239, 2008) but that a critique of the view nevertheless raises significant moral issues within computing such as the ethical goals of a computing (...) profession, the appropriate ethical stance toward bugs, and the public good with respect to computing (Miller, JSEE 14(2):245–249, 2008). Commentators also argue that “A Critique”’s “profitable misreading” demonstrates the “moral ecology” of organizations “dedicated narrowly to financial success” and that other “moral ecologies” that are customer or quality driven can be shown to be more important or preeminent (Huff, JSEE 14(2):241–244, 2008). It is argued here that (1) the hyper-inflated reading of Gotterbarn’s and Ladd’s views on positive responsibility persists despite Gotterbarn’s explicit rejection of it, and that (2) such a reading of positive responsibility cannot be placed within a single moral ecology, nor can a single moral ecology be shown to be any more important or preeminent than others. (shrink)

Purpose – The purpose of this paper is to examine the ethical implications of Google's Knowledge Graph. The paper argues that in the advent and implementation of said Knowledge Graph, the role of Google in users' lives and the power held by Google as the key intermediary of information must be scrutinized. Design/methodology/approach – Revisiting existing literature on Google and its impact on knowledge culture, the paper seeks to assess whether the implementation of The Knowledge Graph represents a significant shift (...) in the nature of the service. Findings – The paper concludes that the extension to Google Search, The Knowledge Graph, can serve to radicalize Google's position as a key intermediary of information in users' lives. Rather than simply serving as a gatekeeper supplying the user with an array of links matching a given query, Google now conveniently disseminates information on their own site, roughly rendering the remainder of the web superfluous. Considering both the commercial nature and the opacity of the service, Google as a de facto solo editor of information is worrying from both a democratic and ethical perspective. A culture of emphatic insistence on convenience and consumption is likely to contribute to the impediment of autonomous information retrieval and digital literacy. Research limitations/implications – The paper must be considered a preliminary inquiry into Google's reliability as an editor of the body of knowledge. As of yet, no literature specifically has remarked on The Knowledge Graph. Originality/value – This paper examines whether the newest extension of Google Search, The Knowledge Graph, poses any significant changes to the assessment of the service and its role in the culture. Fostering critical, digital literacy in search engine users is deemed of even more vital importance to society with the implementation of The Knowledge Graph. This paper, preliminary and far from exhaustive, seeks to initiate a discussion on the future responsibilities of Google, scholars and users in securing the ideal of critical digital literacy. (shrink)

Many seem to think that AI-induced responsibility gaps are morally bad and therefore ought to be avoided. We argue, by contrast, that there is at least a pro tanto reason to welcome responsibility gaps. The central reason is that it can be bad for people to be responsible for wrongdoing. This, we argue, gives us one reason to prefer automated decision-making over human decision-making, especially in contexts where the risks of wrongdoing are high. While we are not the first to (...) suggest that responsibility gaps should sometimes be welcomed, our argument is novel. Others have argued that responsibility gaps should sometimes be welcomed because they can reduce or eliminate the psychological burdens caused by tragic moral choice-situations. By contrast, our argument explains why responsibility gaps should sometimes be welcomed even in the absence of tragic moral choice-situations, and even in the absence of psychological burdens. (shrink)

Big data and artificial intelligence (“AI”) promise to transform virtually all aspects of biomedical research and health care (Matheny et al. 2019), through facilitation of drug development, diagno...

When a computer system causes harm, who is responsible? This question has renewed significance given the proliferation of autonomous systems enabled by modern artificial intelligence techniques. At the root of this problem is a philosophical difficulty known in the literature as the responsibility gap. That is to say, because of the causal distance between the designers of autonomous systems and the eventual outcomes of those systems, the dilution of agency within the large and complex teams that design autonomous systems, and (...) the impossibility of fully predicting how autonomous systems will behave once deployed, determining who is morally responsible for harms caused by autonomous systems is unclear at a conceptual level. I review past work on this topic, criticizing prior works for suggesting workarounds rather than philosophical answers to the conceptual problem presented by the responsibility gap. The view I develop, drawing on my earlier work on vicarious moral responsibility, explains why computing professionals are ethically required to take responsibility for the systems they design, despite not being blameworthy for the harms these systems may cause. (shrink)

It has been claimed that (1) computer professionals should be held responsible for an undisclosed list of “undesirable events” associated with their work and (2) most if not all computer disasters can be avoided by truly understanding responsibility. Programmers, software developers, and other computer professionals should be defended against such vague, counterproductive, and impossible ideals because these imply the mandatory satisfaction of social needs and the equation of ethics with a kind of altruism. The concept of social needs is debatable (...) with no one possessing the authority to impose their version of them. Similarly, the notion of “positive responsibility” is difficult to apply, does not effectively change computing practice, and confuses good (i.e., efficient) computer engineering with good (i.e. moral) computer engineering. (shrink)

During the last decade there has been burgeoning research concerning the ways in which we should think of and apply the concept of responsibility for Artificial Intelligence. Despite this conceptual richness, there is still a lack of consensus regarding what Responsible AI entails on both conceptual and practical levels. The aim of this paper is to connect the ethical dimension of responsibility in Responsible AI with Aristotelian virtue ethics, where notions of context and dianoetic virtues play a grounding role for (...) the concept of moral responsibility. The paper starts by highlighting the important difficulties in assigning responsibility to either technologies themselves or to their developers. Top-down and bottom-up approaches to moral responsibility are then contrasted, as we explore how they could inform debates about Responsible AI. We highlight the limits of the former ethical approaches and build the case for classical Aristotelian virtue ethics. We show that two building blocks of Aristotle’s ethics, dianoetic virtues and the context of actions, although largely ignored in the literature, can shed light on how we could think of moral responsibility for both AI and humans. We end by exploring the practical implications of this particular understanding of moral responsibility along the triadic dimensions of ethics by design, ethics in design and ethics for designers. (shrink)

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)

Background Machine learning (ML) is utilized increasingly in health care, and can pose harms to patients, clinicians, health systems, and the public. In response, regulators have proposed an approach that would shift more responsibility to ML developers for mitigating potential harms. To be effective, this approach requires ML developers to recognize, accept, and act on responsibility for mitigating harms. However, little is known regarding the perspectives of developers themselves regarding their obligations to mitigate harms.Methods We conducted 40 semi-structured interviews with (...) developers of ML predictive analytics applications for health care in the United States.Results Participants varied widely in their perspectives on personal responsibility and included examples of both moral engagement and disengagement, albeit in a variety of forms. While most (70%) of participants made a statement indicative of moral engagement, most of these statements reflected an awareness of moral issues, while only a subset of these included additional elements of engagement such as recognizing responsibility, alignment with personal values, addressing conflicts of interests, and opportunities for action. Further, we identified eight distinct categories of moral disengagement reflecting efforts to minimize potential harms or deflect personal responsibility for preventing or mitigating harms.Conclusions These findings suggest possible facilitators and barriers to the development of ethical ML that could act by encouraging moral engagement or discouraging moral disengagement. Regulatory approaches that depend on the ability of ML developers to recognize, accept, and act on responsibility for mitigating harms might have limited success without education and guidance for ML developers about the extent of their responsibilities and how to implement them. (shrink)