Conceptual engineering is the design, evaluation and implementation of concepts. Despite its popularity, some have argued that the methodology is not worthwhile, because the implementation of new concepts is both inscrutable and beyond our control. In the recent literature we see different responses to this worry. Some have argued that it is for political reasons just as well that implementation is such a difficult task, while others have challenged the metasemantic and social assumptions that underlie this skepticism about implementation. In (...) this paper, I argue that even if implementation is as difficult as critics maintain, there is at least one context in which conceptual engineering is extremely impactful and demonstrably so: the design of new technology. Different conceptions of control, freedom, trust, etc. lead to different designs and implementations of systems that are built to embed those concepts. This means that if we want to design for control, freedom, trust, etc., we have to decide which conception we ought to use. When we determine what the appropriate conception of a concept is in a technological context and use this conception to operationalize a norm or value, we generate requirements which have real-world effects. This not only shows that conceptual engineering can be extremely impactful, the fact that it leads to different design requirements means that we have a way to evaluate our conceptual choices and that we can use this feedback loop to improve upon our conceptual work By illustrating the direct impacts of different conceptions on technology design, this paper underscores the practical applicability and value of conceptual engineering, demonstrating how it serves as a crucial bridge between abstract philosophical analysis and concrete technological innovation. (shrink)

Responsibility gaps traditionally refer to scenarios in which no one is responsible for harm caused by artificial agents, such as autonomous machines or collective agents. By carefully examining the different ways this concept has been defined in the social ontology and ethics of technology literature, I argue that our current concept of responsibility gaps is defective. To address this conceptual flaw, I argue that the concept of responsibility gaps should be revised by distinguishing it into two more precise concepts: epistemic (...) responsibility gaps (ERG) and control misalignments (CM). ERG functions as a refined version addressing specific aspects of responsibility attribution. It captures the challenge of identifying who is to blame. CM, on the other hand, introduces a new dimension by focusing on misalignments of control. These revisions offer better guidelines for the development and oversight of artificial agents, both in terms of assigning blame and ensuring the right distribution of control. (shrink)

Raul Hakli & Pekka Mäkelä (2016, 2019) make a popular assumption in machine ethics explicit by arguing that artificial agents cannot be responsible because they are designed. Designed agents, they think, are analogous to manipulated humans and therefore not meaningfully in control of their actions. Contrary to this, I argue that under all mainstream theories of responsibility, designed agents can be responsible. To do so, I identify the closest parallel discussion in the literature on responsibility and free will, which concerns (...) ‘design cases’. Design cases are theoretical examples of agents that appear to lack responsibility because they were designed, philosophers use these cases to explore the relationship between design and responsibility. This paper presents several replies to design cases from the responsibility literature and uses those replies to situate the corresponding positions on the design and responsibility of artificial agents in machine ethics. I argue that each reply can support the design of responsible agents. However, each reply also entails different levels of severity in the constraints for the design of responsible agents. I offer a brief discussion of the nature of those constraints, highlighting the challenges respective to each reply. I conclude that designing responsible agents is possible, with the caveat that the difficulty of doing so will vary according to one’s favoured reply to design cases. (shrink)

Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine’s behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today’s AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make (...) better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call “decision ownership”: they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made. (shrink)

The way in which agency is conceptualised has implications for understanding human–machine interactions and the governance of technology, especially artificial intelligence (AI) systems. Traditionally, agency is conceptualised as a capacity, defined by intrinsic properties, such as cognitive or volitional facilities. I argue that the capacity-based account of agency is inadequate to explain the dynamics of human–machine interactions and guide technology governance. Instead, I propose to conceptualise agency as impact. Agents as impactful entities can be identified at different levels: from the (...) low level of individual entities to the high level of complex socio-technical systems. Entities can impact their surroundings through different channels, and more influential channels of impact lead to higher degrees of agency. Technology governance must take into account different channels of impact in the contexts of use, design and regulation. (shrink)

Online social networks can bridge the gap between distant individuals by simulating online experiences that closely resemble physical interactions. While people have positive experiences, such as joy, in a physical relationship and would like to enjoy those experiences online, they also have negative experiences, such as being subject to a lie. An online social network may allow users to lie to simulate a real-world social group better. However, lying must be prevented on social networks as unethical behavior. Thus, this study (...) aims to investigate an ethical dilemma raised due to the two abovementioned perspectives.,This paper examines different aspects of lying in social networks, using a comprehensive descriptive literature review approach and a classification scheme.,Contrary to their initial intent, social networks are disrupting the traditional notion of human interaction, either by disregarding the presence of dishonesty or by attempting to regulate relationships. In this research, the authors examine the challenges of these two perspectives by identifying enablers, consequences and control measures of lying on social networks at the individual, social and technological levels. The authors also develop a framework to facilitate investigating this dilemma.,This paper examines a controversial topic. Although lying is unethical, allowing users to lie may be considered an appealing feature of social network platforms. This paper offers a framework to propose several research directions for future studies. This study’s examination focuses on the challenges associated with deception on social networks, prompting the formulation of three crucial questions regarding the potential impact of technology on such behavior. (shrink)