Science and Engineering Ethics最新文献

We address an open problem in the philosophy of artificial intelligence (AI): how to justify the epistemic attitudes we have towards the trustworthiness of AI systems. The problem is important, as providing reasons to believe that AI systems are worthy of trust is key to appropriately rely on these systems in human-AI interactions. In our approach, we consider the trustworthiness of an AI as a time-relative, composite property of the system with two distinct facets. One is the actual trustworthiness of the AI and the other is the perceived trustworthiness of the system as assessed by its users while interacting with it. We show that credences, namely, beliefs we hold with a degree of confidence, are the appropriate attitude for capturing the facets of the trustworthiness of an AI over time. Then, we introduce a reliabilistic account providing justification to the credences in the trustworthiness of AI, which we derive from Tang's probabilistic theory of justified credence. Our account stipulates that a credence in the trustworthiness of an AI system is justified if and only if it is caused by an assessment process that tends to result in a high proportion of credences for which the actual and perceived trustworthiness of the AI are calibrated. This approach informs research on the ethics of AI and human-AI interactions by providing actionable recommendations on how to measure the reliability of the process through which users perceive the trustworthiness of the system, investigating its calibration to the actual levels of trustworthiness of the AI as well as users' appropriate reliance on the system.

In this paper, we suggest that personalized LLMs trained on information written by or otherwise pertaining to an individual could serve as artificial moral advisors (AMAs) that account for the dynamic nature of personal morality. These LLM-based AMAs would harness users' past and present data to infer and make explicit their sometimes-shifting values and preferences, thereby fostering self-knowledge. Further, these systems may also assist in processes of self-creation, by helping users reflect on the kind of person they want to be and the actions and goals necessary for so becoming. The feasibility of LLMs providing such personalized moral insights remains uncertain pending further technical development. Nevertheless, we argue that this approach addresses limitations in existing AMA proposals reliant on either predetermined values or introspective self-knowledge.

Citizen science (CS) is an umbrella term for research with a significant amount of contributions from volunteers. Those volunteers can occupy a hybrid role, being both 'researcher' and 'subject' at the same time. This has repercussions for questions about responsibility and credit, e.g. pertaining to the issue of authorship. In this paper, we first review some existing guidelines for authorship and their applicability to CS. Second, we assess the claim that the guidelines from the International Committee of Medical Journal Editors (ICMJE), known as 'the Vancouver guidelines', may lead to exclusion of deserving citizen scientists as authors. We maintain that the idea of including citizen scientists as authors is supported by at least two arguments: transparency and fairness. Third, we argue that it might be plausible to include groups as authors in CS. Fourth and finally, we offer a heuristic list of seven recommendations to be considered when deciding about whom to include as an author of a CS publication.

Any moral algorithm for autonomous vehicles must provide a practical solution to moral problems of the trolley type, in which all possible courses of action will result in damage, injury, or death. This article discusses a hitherto neglected variety of this type of problem, based on a recent psychological study whose results are reported here. It argues that the most adequate solution to this problem will be achieved by a moral algorithm that is based on Confucian ethics. In addition to this philosophical and psychological discussion, the article outlines the mathematics, engineering, and legal implementation of a possible Confucian algorithm. The proposed Confucian algorithm is based on the idea of making it possible to set an autonomous vehicle to allow an increased level of protection for selected people. It is shown that the proposed algorithm can be implemented alongside other moral algorithms, using either the framework of personal ethics settings or that of mandatory ethics settings.

To facilitate engineering students' understanding of engineering ethics and support instructors in developing course content, this study introduces an innovative educational tool drawing inspiration from the Rubik's Cube metaphor. This Engineering Ethics Knowledge Rubik's Cube (EEKRC) integrates six key aspects-ethical theories, codes of ethics, ethical issues, engineering disciplines, stakeholders, and life cycle-identified through an analysis of engineering ethics textbooks and courses across the United States, Singapore, and China. This analysis underpins the selection of the six aspects, reflecting the shared and unique elements of engineering ethics education in these regions. In an engineering ethics course, the EEKRC serves multiple functions: it provides visual support for grasping engineering ethics concepts, acts as a pedagogical guide for both experienced and inexperienced educators in course design, offers a complementary assessment method for evaluating students learning outcomes, and assists as a reference for students engaging in ethical analysis.

In this paper, I argue that patient preferences concerning human physical attributes associated with race, culture, and gender should be excluded from public healthcare robot design. On one hand, healthcare should be (objective, universal) needs oriented. On the other hand, patient well-being (the aim of healthcare) is, in concrete ways, tied to preferences, as is patient satisfaction (a core WHO value). The shift toward patient-centered healthcare places patient preferences into the spotlight. Accordingly, the design of healthcare technology cannot simply disregard patient preferences, even those which are potentially morally problematic. A method for handling these at the design level is thus imperative. By way of uncontroversial starting points, I argue that the priority of the public healthcare system is the fulfillment of patients' therapeutic needs, among which certain potentially morally problematic preferences may be counted. There are further ethical considerations, however, which, taken together, suggest that the potential benefits of upholding these preferences are outweighed by the potential harms.

Danaher (2016) has argued that increasing robotization can lead to retribution gaps: Situations in which the normative fact that nobody can be justly held responsible for a harmful outcome stands in conflict with our retributivist moral dispositions. In this paper, we report a cross-cultural empirical study based on Sparrow's (2007) famous example of an autonomous weapon system committing a war crime, which was conducted with participants from the US, Japan and Germany. We find that (1) people manifest a considerable willingness to hold autonomous systems morally responsible, (2) partially exculpate human agents when interacting with such systems, and that more generally (3) the possibility of normative responsibility gaps is indeed at odds with people's pronounced retributivist inclinations. We discuss what these results mean for potential implications of the retribution gap and other positions in the responsibility gap literature.

The tutelage of our mentors as scientists included the analogy that writing a good scientific paper was an exercise in storytelling that omitted unessential details that did not move the story forward or that detracted from the overall message. However, the advice to not get lost in the details had an important flaw. In science, it is the many details of the data themselves and the methods used to generate and analyze them that give conclusions their probative meaning. Facts may sometimes slow or distract from the clarity, tidiness, intrigue, or flow of the narrative, but nevertheless they are important for the assessment of what was done, the trustworthiness of the science, and the meaning of the findings. Nevertheless, many critical elements and facts about research studies may be omitted from the narrative and become hidden from scholarly scrutiny. We describe a "baker's dozen" shortfalls in which such elements that are pertinent to evaluating the validity of scientific studies are sometimes hidden in reports of the work. Such shortfalls may be intentional or unintentional or lie somewhere in between. Additionally, shortfalls may occur at the level of the individual or an institution or of the entire system itself. We conclude by proposing countermeasures to these shortfalls.

Self-driving vehicles (SDVs) will need to make decisions that carry ethical dimensions and are of normative significance. For example, by choosing a specific trajectory, they determine how risks are distributed among traffic participants. Accordingly, policymakers, standardization organizations and scholars have conceptualized what (shall) constitute(s) ethical decision-making for SDVs. Eventually, these conceptualizations must be converted into specific system requirements to ensure proper technical implementation. Therefore, this article aims to translate critical requirements recently formulated in scholarly work, existing standards, regulatory drafts and guidelines into an explicit five-step ethical decision model for SDVs during hazardous situations. This model states a precise sequence of steps, indicates the guiding ethical principles that inform each step and points out a list of terms that demand further investigation and technical specification. By integrating ethical, legal and engineering considerations, we aim to contribute to the scholarly debate on computational ethics (particularly in autonomous driving) while offering practitioners in the automotive sector a decision-making process for SDVs that is technically viable, legally permissible, ethically grounded and adaptable to societal values. In the future, assessing the actual impact, effectiveness and admissibility of implementing the here sketched theories, terms and the overall decision process requires an empirical evaluation and testing of the overall decision-making model.

The popularisation of Artificial Intelligence (AI) technologies has sparked discussion about their ethical implications. This development has forced governmental organisations, NGOs, and private companies to react and draft ethics guidelines for future development of ethical AI systems. Whereas many ethics guidelines address values familiar to ethicists, they seem to lack in ethical justifications. Furthermore, most tend to neglect the impact of AI on democracy, governance, and public deliberation. Existing research suggest, however, that AI can threaten key elements of western democracies that are ethically relevant. In this paper, Rawls's theory of justice is applied to draft a set of guidelines for organisations and policy-makers to guide AI development towards a more ethical direction. The goal is to contribute to the broadening of the discussion on AI ethics by exploring the possibility of constructing AI ethics guidelines that are philosophically justified and take a broader perspective of societal justice. The paper discusses how Rawls's theory of justice as fairness and its key concepts relate to the ongoing developments in AI ethics and gives a proposition of how principles that offer a foundation for operationalising AI ethics in practice could look like if aligned with Rawls's theory of justice as fairness.