Computer Ethics

In a recent thought-provoking essay called “ChatGPT is Bullshit,” Hicks, Humphries and Slater call such large language models (LLMs) “bullshitters” and “bullshit machines.” Unlike the term “bullshit,” they argue, commonly used anthropomorphic terms such as “hallucination” and “confabulation” mispresent LLMs and sow confusion that could be socially harmful. This paper criticizes their essay in two steps. First, its reliance on Harry Frankfurt’s classic characterization of bullshit as indifference to truth, though understandable and compelling in one sense, risks misrepresenting LLMs. Second, (...) the argument is too quick to jettison anthropomorphic terms like hallucination and confabulation, which might prove useful metaphors for understanding generative AI. Exploring language to articulate good ways of understanding LLMs is indeed a socially important task, one benefitting from critical open-mindedness, some historical awareness, and a nuanced approach to how various words used to describe AI can operate. This paper attempts to contribute to this task by questioning the wisdom of categorically calling bullshit on ChatGPT. (shrink)

A multimillion-dollar digital voter deterrence ad industry has emerged in the United States. Beginning with the 2016 US Presidential Election, interested parties ranging from international intelligence agencies to campaigning politicians have enlisted social media platforms’ microtargeted advertising infrastructure to inundate certain voter demographics with anti-voting content. These tactics are used disproportionately against people of color, especially Black voters, and remain virtually unregulated and unaddressed. This paper responds to the issue in two ways. First, I strive to catalyze a broader awareness (...) of this practice and the intertwined moral and epistemic wrongs that it entails. The wrongs of this digital industry occur simultaneously at three levels: individually, these types of ads undermine autonomy; socially, they constitute the perpetration of discriminatory voter suppression and epistemic injustice; and historically, they reify racial inequality by perpetuating targeted disenfranchisement efforts against non-white voters. Second, I argue that this type of content is instructive for the ongoing debate regarding social media platforms’ moderation of online political speech. The arguments I develop here provide a compelling case for the moderation of political content that seeks to disenfranchise, especially when those efforts are undertaken in discriminatory ways. This holds urgent implications for how social media platforms moderate and profit from suppressive content. (shrink)

This study examines how ethical standards established by stakeholders such as developers and policymakers provide top-down guidance aligned with deontological ethics or utilitarian goals. It also highlights a complementary bottom-up approach, rooted in virtue ethics, in which individuals engage in ethical deliberations shaped by their moral values. Both approaches have limitations, and, at times, ethical standards can clash with moral values, thus blurring lines of responsibilities. Deontological principles may offer a structured framework, but often lack adaptability to diverse cultural contexts; (...) bottom-up approaches foster intrinsic moral intentions, but universal applicability may be challenging, thus raising moral dilemmas. Through a theoretical literature review, this study explains how different ontological and normative ethical perceptions lead to moral dilemmas in various AI application scenarios (e.g., algorithmically managed platforms, crime detection systems, medical AI assistants). It addresses top-down and bottom-up approaches that may help account for moral dilemmas ethically. The study discusses the balance between top-down regulatory frameworks and bottom-up community-driven ethics to navigate the complex ethical landscape of AI applications, whose increasing capabilities alter expectations of AI’s agency and morality. This study calls for holistic and multi-objective ethical frameworks that incorporate diverse normative ethical perspectives and recognizes context-specific ontologies throughout the AI lifecycle. It emphasizes a nuanced and context-specific combination of top-down standards (e.g., regulatory oversight, clear guidelines) and bottom-up fostering of moral values (e.g., by improving ethical knowledge). This tailored and ongoing reflection of ethical standards and moral values accounts for an ethical development, deployment, and utilization of AI technologies. (shrink)

It has been extensively argued that emerging autonomous technologies can represent a challenge for our traditional responsibility practices. Though these challenges differ in a variety of ways, at the center of these challenges is the worrying possibility that there may be outcomes of autonomous technologies for which there are legitimate demands for responsibility but no legitimate target to bear this responsibility. This is well exemplified by the possibility of techno-responsibility gaps. These challenges have elicited a number of responses, including dismissals (...) of the legitimacy of these demands, attempts to find proximate agents that can be legitimately held responsible, and arguments for prohibiting the use of technologies that may open such gaps. In this piece we present a general argument that an overlooked but valuable option lies in adopting a strategy of taking responsibility for the outcomes of autonomous technologies even when the conditions for being legitimately held responsible are not met. We develop a general argument that the adoption of such a strategy is often justified not only by the demands of being responsible, but by practical considerations rooted in our relationships: the need to preserve of the quality of our relationships and the trustworthiness of the socio-technical system of which the autonomous technology is both a result of and embedded in. (shrink)

Machine learning (ML) systems are vulnerable to performance decline over time due to dataset shift. To address this problem, experts often suggest that ML systems should be regularly updated to ensure ongoing performance stability. Some scholarly literature has begun to address the epistemic and ethical challenges associated with different updating methodologies. Thus far, however, little attention has been paid to the impact of model updating on the ML-assisted decision-making process itself. This article aims to address this gap. It argues that (...) model updating introduces a new sub-type of opacity into ML-assisted decision-making—update opacity—that occurs when users cannot understand how or why an update has changed the reasoning or behaviour of an ML system. This type of opacity presents a variety of distinctive epistemic and safety concerns that available solutions to the black box problem in ML are largely ill-equipped to address. A variety of alternative strategies may be developed or pursued to address the problem of update opacity more directly, including bi-factual explanations, dynamic model reporting, and update compatibility. However, each of these strategies presents its own risks or carries significant limitations. Further research will be needed to address the epistemic and safety concerns associated with model updating and update opacity going forward. (shrink)

This paper considers how act consequentialism can engage with the Gamer’s Dilemma (the seeming moral distinction between virtual murder and virtual child molestation in video games). I argue that often the dilemma is implicitly or explicitly framed in a way that presumes that an answer can only be in terms of action types rather than tokens. This framing unfairly stacks the deck against act consequentialist approaches. An act consequentialist approach to the Gamer’s Dilemma speaks to its own set of concerns, (...) and this paper proposes three ways consequentialists can address the dilemma: adopting rule utilitarianism, taking a regulative approach focused on laws and policies, or examining typical cases to develop heuristics. Each of these approaches have advantages and disadvantages, and it’s important to recognize what the specific concerns of each approach are, as well as their particular strengths and limitations. By clarifying these different consequentialist framings, I demonstrate ways in which consequentialism can meaningfully contribute to debates on the ethics of virtual actions. I conclude by outlining some methodological benefits of bringing clarity to the particular way(s) that consequentialism can speak to the Gamer’s Dilemma and suggest how existing discussions using deontological and virtue ethical approaches might be productively reframed in consequentialist terms. (shrink)

Customer ratings and reviews are playing a key role in the contemporary platform economy. To establish trust among stran- gers without having to directly monitor platform users themselves, companies ask people to evaluate each other. Firms like Uber, Deliveroo, or Airbnb construct digital reputation scores by combining these consumer data with their own information from the algorithmic surveillance of workers. Trustworthy behavior is subsequently rewarded with a good reputation score and higher potential earnings, while untrustworthy behavior can be algorithmically penalized. (...) However, can these technolo- gies themselves be trusted? Are they reliable communicators of trustworthy market behavior and do they fairly distribute the benefits and burdens of the online economy among platform companies, consumers, and workers? The implementation of Digital Reputation Systems (DRSs) has obvious advantages, like reducing adjudication costs and enhancing consumer trust, but there are also disadvantages that are predominantly offloaded on workers. Taking Airbnb’s DRS as my case study, I use Stephan Lessenich’s theory of the externalization society to argue that DRSs concentrate the benefits of the platform economy among consumers and platform companies while pushing negative externalities mostly unto workers. (shrink)

Authorship Attribution (AA) approaches in Natural Language Processing (NLP) are important in various domains, including forensic analysis and cybercrime. However, they pose Ethical, Legal, and Societal Implications/Aspects (ELSI/ELSA) challenges that remain underexplored. Inspired by foundational AI ethics guidelines and frameworks, this research introduces a comprehensive framework of responsible guidelines that focuses on AA tasks in NLP, which are tailored to different stakeholders and development phases. These guidelines are structured around four core principles: privacy and data protection, fairness and non-discrimination, transparency (...) and explainability, and societal impact. Furthermore, to illustrate a practical application of our guidelines, we apply them to a recent AA study that targets identifying and linking potential human trafficking vendors. We believe the proposed guidelines can assist researchers and practitioners in justifying their decisions, assisting ethical committees in promoting responsible practices, and identifying ethical concerns related to NLP-based AA approaches. Our study aims to contribute to ensuring the responsible development and deployment of AA tools. (shrink)

Much of the work in the rapidly growing field of computer ethics relies on the concepts and theories of Western philosophy. With this article my aim is to help stimulate conversations that draw on a wider range of ethical perspectives. I build on recent work on the sense in which the regular operations of the attention economy might violate our right to attention, and I do so through looking to a range of Theravāda and Mahāyāna Buddhist texts. As I argue, (...) these texts should inspire us to realize that we have more than just the right to direct our attention as we will and the right to be free from distraction. This is because there is a third right to attention that the recent literature overlooks, namely the right to strengthen our weak capacity to control our attention. (shrink)

In Cultural Analytics, Lev Manovich (2020) outlines the recent developments and the historical roots of a new, exciting research field called cultural analytics. Cultural analytics emerges as a discipline that utilizes methods from computer science, data visualization, and media arts for the exploration and analysis of cultural objects and their user interactions. Manovich continuously admonishes future researchers to think hard about the challenges of how cultural phenomenon can be represented as data to avoid the reductivism trap, as he quotes Gitelman (...) and Jackson: “our zeal for more and more data can become a faith in their neutrality and autonomy, their objectivity. . .” Clearly, big, clever, and rigorous data must be the foundation for the advance of science in the 21st century. -/- ".” Her entire being is like an ethereal painting, a playful creation of nature using colors, lines, dots, strokes, and patches. One can only exclaim, “Absolutely enchanting. Is this real or an illusion?" - In The Philosophy of Awakening, Wild Wise Weird: The Kingfisher Story Collection, Vuong (2025)- . (shrink)

Most people who have considered the matter have concluded that machines cannot be moral agents. Responsibility for acting on the outputs of machines must always rest with a human being. A key problem for the ethical use of AI, then, is to ensure that it does not block the attribution of responsibility to humans or lead to individuals being unfairly held responsible for things over which they had no control. This is the “responsibility gap”. In this paper, we argue that (...) the claim that machines cannot be held responsible for their actions has unacknowledged implications for the conditions under which the outputs of AI can serve as reasons for belief. Following Robert Brandom, we argue that, because the assertion of a claim is an action, moral agency is a necessary condition for the giving and evaluating of reasons in discourse. Thus, the same considerations that suggest that machines cannot be held responsible for their actions suggest that they cannot be held to account for the epistemic value — or lack of value — of their outputs. If there is a responsibility gap, there is also a “testimony gap.” An under-recognised problem with the use of AI, then, is to ensure that it does not block the attribution of testimony to human beings or lead to individuals being held responsible for claims that they have not asserted. More generally, the “assertions” of machines are only capable of serving as justifications for belief or action where one or more people accept responsibility for them. (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)

This paper engages the ongoing debate around the possibility of virtue friendships in the Aristotelian sense through online mediation. However, I argue that since the current literature has remained overly focused on the mere possibility of virtual friendship, it has obscured the more common phenomena of using digital communication to sustain previous in-person friendships which are now at a distance. While I agree with those who argue that entirely virtual friendship is possible, I argue that the current rebuttals to the (...) objections regarding embodiment are lacking. We can see this more clearly when we shift our consideration to more ordinary cases of virtual friendship. I discuss this problem of disembodiment by engaging with an interpretive issue of Aristotle’s “living together” which has played a key role in the current literature. I then turn to expand the current debate by examining how physical absence, disembodiment, and the technology we use to overcome such disembodiment, shapes and impacts friendships at a distance. In short, I explore the tendencies of technology of which virtual friends ought to be aware. I argue that even though virtual virtue friendships are possible via technology, the medium makes it harder and limits the possible experiences within friendships. Even if we overcome these tendencies, technologically mediated virtue friendships require more discipline for a diminished experience. While these limitations and extra disciplines in no way make virtual friendship unimportant, I argue that the fullest possible virtual friendship will be lesser than the fullest possible traditional friendship. (shrink)

Is the pool of fair predictive algorithms fair? It depends, naturally, on both the criteria of fairness and on how we pool. We catalog the relevant facts for some of the most prominent statistical criteria of algorithmic fairness and the dominant approaches to pooling forecasts: linear, geometric, and multiplicative. Only linear pooling, a format at the heart of ensemble methods, preserves any of the central criteria we consider. Drawing on work in the social sciences and social epistemology on the theoretical (...) foundations of the wisdom of crowds, we explain how our observations present an exception to the general trend of finding tradeoffs between the accuracy and fairness of forecasts. (shrink)

This paper investigates how human interactions with AI-powered chatbots may offend human dignity. Current chatbots, driven by large language models (LLMs), mimic human linguistic behaviour but lack the moral and rational capacities essential for genuine interpersonal respect. Human beings are prone to anthropomorphise chatbots—indeed, chatbots appear to be deliberately designed to elicit that response. As a result, human beings’ behaviour toward chatbots often resembles behaviours typical of interaction between moral agents. Drawing on a second-personal, relational account of dignity, we argue (...) that interacting with chatbots in this way is incompatible with the dignity of users. We show that, since second-personal respect is premised on reciprocal recognition of second-personal moral authority, behaving towards chatbots in ways that convey second-personal respect is bound to misfire in morally problematic ways, given the lack of reciprocity. Consequently, such chatbot interactions amount to subtle but significant violations of self-respect—the respect we are dutybound to show for our own dignity. We illustrate this by discussing four actual chatbot use cases (information retrieval, customer service, advising, and companionship), and propound that the increasing societal pressure to engage in such interactions with chatbots poses a hitherto underappreciated threat to human dignity. (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)

The conventional discourse on existential risks (x-risks) from AI typically focuses on abrupt, dire events caused by advanced AI systems, particularly those that might achieve or surpass human-level intelligence. These events have severe consequences that either lead to human extinction or irreversibly cripple human civilization to a point beyond recovery. This decisive view, however, often neglects the serious possibility of AI x-risk manifesting gradually through an incremental series of smaller yet interconnected disruptions, crossing critical thresholds over time. This paper contrasts (...) the conventional decisive AI x-risk hypothesis with what I call an accumulative AI x-risk hypothesis. While the former envisions an overt AI takeover pathway, characterized by scenarios like uncontrollable superintelligence, the latter suggests a different pathway to existential catastrophes. This involves a gradual accumulation of AI-induced threats such as severe vulnerabilities and systemic erosion of critical economic and political structures. The accumulative hypothesis suggests a boiling frog scenario where incremental AI risks slowly undermine systemic and societal resilience until a triggering event results in irreversible collapse. Through complex systems analysis, this paper examines the distinct assumptions differentiating these two hypotheses. It is then argued that the accumulative view can reconcile seemingly incompatible perspectives on AI risks. The implications of differentiating between the two types of pathway—the decisive and the accumulative—for the governance of AI as well as long-term AI safety are discussed. -/- . (shrink)

We investigate a typology of socially and ethically risky phenomena emerging from the interaction between humans and large language model (LLM)-based conversational systems. As they relate to the way in which humans attribute social identity components, such as role and face, to LLM-based conversational systems, we term these phenomena 'social misattributions.' Drawing on classical theories of social identity and recent debates in the philosophy of technology, we argue that these social misattributions represent higher-order forms of anthropomorphisation of LLM-based conversational systems (...) that are not justified by their technical capabilities and follow from the social context of conversational interactions. We discuss the risks these misattributions pose to human users, including emotional manipulation and unwarranted trust, and propose mitigation strategies. Our recommendations emphasise the importance of fostering social transparency and exploring approaches, such as frictional design, that are currently promoted in the research domain of human-centred artificial intelligence. (shrink)

Technology companies are increasingly being asked to take responsibility for the technologies they create. Many of them are rising to the challenge. One way they do this is by implementing “guardrails”: restrictions on functionality that prevent people from misusing their technologies (per some standard of misuse). While there can be excellent reasons for implementing guardrails (and doing so is sometimes morally obligatory), I argue that the unrestricted authority to implement guardrails is incompatible with proper respect for user freedom, and is (...) not something we should welcome. I argue instead that guardrails should be implemented for only two reasons: to prevent accidental misuse of the technology, and as a proportionate means of preventing people from using the technology to violate other people’s rights. If I’m right, then we may have to get more comfortable with developers releasing technologies that can, and to some extent inevitably will, be misused; people using technologies in ways we disagree with is one of the costs of liberty, but it is a cost we have excellent reasons to bear. (shrink)

The aim of this chapter is to explore the safety value of implementing Asimov’s Laws of Robotics as a future general framework that humans should obey. Asimov formulated laws to make explicit the safeguards of the robots in his stories: (1) A robot may not injure or harm a human being or, through inaction, allow a human being to come to harm; (2) A robot must obey the orders given to it by human beings except where such orders would conflict (...) with the First Law; (3) A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. In Asimov’s stories, it is always assumed that the laws are built into the robots to govern the behaviour of the robots. As his stories clearly demonstrate, the Laws can be ambiguous. Moreover, the laws are not very specific. General rules as a guide for robot behaviour may not be a very good method to achieve robot safety – if we expect the robots to follow them. But would it work for humans? In this chapter, we ask whether it would make as much, or more, sense to implement the laws in human legislation with the purpose of governing the behaviour of people or companies that develop, build, market or use AI, embodied in robots or in the form of software, now and in the future. (shrink)

In a bid to boost their below-replacement fertility levels, some countries, such as China, India, Iran, and Japan, have launched state-sponsored dating apps, with more potentially following. However, the use of dating apps as tools for social engineering has been largely neglected by political theorists and public policy experts. This article fills this gap. While acknowledging the risks and historical baggage of social engineering, the article provides a qualified defense of using these apps for three purposes: raising below-replacement birth rates, (...) increasing social cohesion, and preventing certain genetic diseases. (shrink)

In this article, firstly, cooperative interaction between robots and humans is discussed; specifically, the possibility of human/robot joint action and (relatedly) the possibility of robots occupying institutional roles alongside humans. The discussion makes use of concepts developed in social ontology. Secondly, certain key moral (or ethical—these terms are used interchangeably here) issues arising from this cooperative action are discussed, specifically issues that arise from robots performing (including qua role occupants) morally significant actions jointly with humans. Such morally significant human/robot joint (...) actions, supposing they exist, could potentially range from humans and robots jointly caring for the infirm through to jointly killing enemy combatants. (shrink)

With the rapid expansion of parameters in large language models (LLMs) and the application of Reinforcement Learning with Human Feedback (RLHF), there has been a noticeable growth in the moral competence of LLMs. However, several questions warrant further exploration: Is it really possible for LLMs to fully align with human values through RLHF? How can the current moral growth be philosophically contextualized? We identify similarities between LLMs’ moral growth and Deweyan ethics in terms of the discourse of human moral development. (...) We then attempt to use Dewey’s theory on an experimental basis to examine and further explain the extent to which the current alignment pathway enables the development of LLMs. A beating experiment serves as the foundational case for analyzing LLMs’ moral competence across various parameters and stages, including basic moral cognition, moral dilemma judgment, and moral behavior. The results demonstrate that the moral competence of the GPT series has seen a significant improvement, and Dewey’s Impulse-Habit-Character theory of moral development can be used to explain this: the moral competence of LLMs has been enhanced through experience-based learning, supported by human feedback. Nevertheless, LLMs’ moral development through RLHF remains constrained and does not reach the character stage described by Dewey, possibly due to their lack of self-consciousness. This fundamental difference between humans and LLMs underscores both the limitations of LLMs’ moral growth and the challenges of applying RLHF for AI alignment. It also emphasizes the need for external societal governance and legal regulation. (shrink)

The paper develops a conception of the good life within a digitalized society. Martha Nussbaum’s capability theory offers an adequate normative framework for that purpose as it systematically integrates the analysis of flourishing human lives with a normative theory of justice. The paper argues that a theory of good digital lives should focus on everyday life, on the impact digitalization has on ordinary actions, routines and corresponding practical knowledge. Based on Nussbaum’s work, the paper develops a concept of digital capabilities. (...) Digital capabilities are combined capabilities: To possess a digital capability, an individual must acquire certain skills and abilities (internal capabilities) and needs access to devices and external infrastructures like internet connections. If societies as a whole and everyday environments are digitalized to a certain degree, the possession of specific digital capabilities is a crucial precondition for a flourishing life. The paper likewise analyzes challenges that are connected to digital capabilities. Digital structures are constantly changing. In consequence, digital capabilities are never acquired once and for all, but always precarious and in danger of being lost—with serious consequences for individual everyday lives in digitalized environments. As digital capabilities are crucial for leading a good life, people are entitled to develop and maintain them. They describe demands of justice. Using the examples of filling in an online form and digital education, the paper finally illustrates the size of institutional changes that are necessary to meet these demands. (shrink)

Who is responsible for a harm caused by AI, or a machine or system that relies on artificial intelligence? Given that current AI is neither conscious nor sentient, it’s unclear that AI itself is responsible for it. But given that AI acts independently of its developer or user, it’s also unclear that the developer or user is responsible for the harm. This gives rise to the so-called responsibility gap: cases where AI causes a harm, but no one is responsible for (...) it. Two central questions in the literature are whether responsibility gaps exist, and if yes, whether they’re morally problematic in a way that counts against developing or using AI. While some authors argue that responsibility gaps exist, and they’re morally problematic, some argue that they don’t exist. In this paper, I defend a novel position. First, I argue that current AI doesn’t generate a new kind of concern about responsibility that the older technologies don’t. Then, I argue that responsibility gaps exist but they’re unproblematic. (NOTE: Email me for a copy.). (shrink)

Research under real-world conditions is crucial to the development and deployment of robust AI systems. Exposing large language models to complex use settings yields knowledge about their performance and impact, which cannot be obtained under controlled laboratory conditions or through anticipatory methods. This epistemic need for real-world research is exacerbated by large-language models’ opaque internal operations and potential for emergent behavior. However, despite its epistemic value and widespread application, the ethics of real-world AI research has received little scholarly attention. To (...) address this gap, this paper provides an analysis of real-world research with LLMs and generative AI, assessing both its epistemic value and ethical concerns such as the potential for interpersonal and societal research harms, the increased privatization of AI learning, and the unjust distribution of benefits and risks. This paper discusses these concerns alongside four moral principles influencing research ethics standards: non-maleficence, beneficence, respect for autonomy, and distributive justice. I argue that real-world AI research faces challenges in meeting these principles and that these challenges are exacerbated by absent or imperfect current ethical governance. Finally, I chart two distinct but compatible ways forward: through ethical compliance and regulation and through moral education and cultivation. (shrink)

Explanations are conceived to ensure the trustworthiness of AI systems. Yet, relying solemnly on algorithmic solutions, as provided by explainable artificial intelligence (XAI), might fall short to account for sociotechnical risks jeopardizing their factuality and informativeness. To mitigate these risks, we delve into the complex landscape of ethical risks surrounding XAI systems and their generated explanations. By employing a literature review combined with rigorous thematic analysis, we uncover a diverse array of technical risks tied to the robustness, fairness, and evaluation (...) of XAI systems. Furthermore, we address a broader range of contextual risks jeopardizing their security, accountability, reception alongside other cognitive, social, and ethical concerns of explanations. We advance a multi-layered risk assessment framework, where each layer advances strategies for practical intervention, management, and documentation of XAI systems within organizations. Recognizing the theoretical nature of the framework advanced, we discuss it in a conceptual case study. For the XAI community, our multifaceted investigation represents a path to practically address XAI risks while enriching our understanding of the ethical ramifications of incorporating XAI in decision-making processes. (shrink)

Recent claims that artificial intelligence (AI) systems demonstrate “emergent abilities” have fueled excitement but also fear grounded in the prospect that such systems may enable a wider range of parties to make unprecedented advances in areas that include the development of chemical or biological weapons. Ambiguity surrounding the term “emergent abilities” has added avoidable uncertainty to a topic that has the potential to destabilize the strategic landscape, including the perception of key parties about the viability of nonproliferation efforts. To avert (...) these problems in the future, scientists, developers, policymakers, and other parties should take credible steps to strengthen the health of the scientific ecosystem around AI. (shrink)

The human factor in information systems is a large vulnerability when implementing cybersecurity, and many approaches, including technical and policy driven solutions, seek to mitigate this vulnerability. Decisions to apply technical or policy solutions must consider how an individual’s values and moral stance influence their responses to these implementations. Our research aims to evaluate how individuals prioritise different ethical principles when making cybersecurity sensitive decisions and how much perceived choice they have when doing so. Further, we sought to use participants’ (...) responses to cybersecurity scenarios to create profiles that describe their values and individual factors including personality. Participants (n = 193) in our study responded to five different ethically sensitive cybersecurity scenarios in random order, selecting their action in that scenario and rating and ranking of the ethical principles (i.e., Beneficence, Non-Maleficence, Justice, Autonomy, Explicability) behind that action. Using participants’ demographics, personality, values, and cyber hygiene practices, we created profiles using machine learning to predict participants’ choices and the principle of most importance to them across scenarios. Further, we found that, generalising, for our participants Autonomy was the most important ethical principle in our scenarios, followed by Justice. Our study also suggests that participants felt they had some agency in their decision making and they were able to weigh up different ethical principles. (shrink)

Urban Digital Twins (UDTs) have become the new buzzword for researchers, planners, policymakers, and industry experts when it comes to designing, planning, and managing sustainable and efficient cities. It encapsulates the last iteration of the technocratic and ultra-efficient, post-modernist vision of smart cities. However, while more applications branded as UDTs appear around the world, its conceptualization remains ambiguous. Beyond being technically prescriptive about what UDTs are, this article focuses on their aspects of interaction and operationalization in connection to people in (...) cities, and how enhanced by metaverse ideas they can deepen societal divides by offering divergent urban experiences based on different stakeholder preferences. Therefore, firstly this article repositions the term UDTs by comparing existing concrete and located applications that have a focus on interaction and participation, including some that may be closer to the concept of UDT than is commonly assumed. Based on the components found separately in the different studied cases, it is possible to hypothesize about possible future, more advanced realizations of UDTs. This enables us to contrast their positive and negative societal impacts. While the development of new immersive interactive digital worlds can improve planning using collective knowledge for more inclusive and diverse cities, they pose significant risks not only the common ones regarding privacy, transparency, or fairness, but also social fragmentation based on urban digital multiplicities. The potential benefits and challenges of integrating this multiplicity of UDTs into participatory urban governance emphasize the need for human-centric approaches to promote socio-technical frameworks able to mitigate risks as social division. (shrink)

This paper examines the ontological and epistemological implications of artificial intelligence (AI) through posthumanist philosophy, integrating the works of Deleuze, Foucault, and Haraway with contemporary computational methodologies. It introduces concepts such as negative augmentation, praxes of revealing, and desedimentation, while extending ideas like affirmative cartographies, ethics of alterity, and planes of immanence to critique anthropocentric assumptions about identity, cognition, and agency. By redefining AI systems as dynamic assemblages emerging through networks of interaction and co-creation, the paper challenges traditional dichotomies such (...) as human versus machine and subject versus object. Bridging analytic and continental philosophical traditions, the analysis unites formal tools like attribution analysis and causal reasoning with the interpretive and processual methodologies of continental thought. This synthesis deepens the understanding of AI’s epistemic and ethical dimensions, expanding philosophical inquiry while critiquing anthropocentrism in AI design. The paper interrogates the spatial foundations of AI, contrasting Euclidean and non-Euclidean frameworks to examine how optimization processes and adversarial generative models shape computational epistemologies. Critiquing the reliance on Euclidean spatial assumptions, it positions alternative geometries as tools for modeling complex, recursive relationships. Furthermore, the paper addresses the political dimensions of AI, emphasizing its entanglements with ecological, technological, and sociopolitical systems that perpetuate inequality. Through a politics of affirmation and intersectional approaches, it advocates for inclusive frameworks that prioritize marginalized perspectives. The concept of computational qualia is also explored, highlighting how subjective-like dynamics emerge within AI systems and their implications for ethics, transparency, and machine perception. Finally, the paper calls for a posthumanist framework in AI ethics and safety, emphasizing interconnectivity, plurality, and the transformative capacities of machine intelligence. This approach advances epistemic pluralism and reimagines the boundaries of intelligence in the digital age, fostering novel ontological possibilities through the co-creation of dynamic systems. (shrink)

Digitalisation has lent the right to privacy increasing philosophical and legal relevance. However, privacy’s epistemic status and associated normative values are constantly subject to radical criticisms. This article investigates the validity, in theory and practice, of three radical critiques of privacy. A review of the philosophical and interdisciplinary discourse on privacy during the last half century is followed by analyses of recent legal developments within the EU. Privacy emerges as a highly differentiated and powerful tool to protect individuals and social (...) relations and to limit and redistribute power. However, the right to privacy remains far from realising its practical potential. (shrink)

This paper examines the ongoing challenges of interdisciplinary collaboration in Machine Ethics (ME), particularly the integration of ethical decision-making capacities into AI systems. Despite increasing demands for ethical AI, ethicists often remain on the sidelines, contributing primarily to metaethical discussions without directly influencing the development of moral machines. This paper revisits concerns highlighted by Tolmeijer et al. (2020), who identified the pitfall that computer scientists may misinterpret ethical theories without philosophical input. Using the MACHIAVELLI moral benchmark and the Delphi artificial (...) moral agent as case studies, we analyze how these challenges persist. Our analysis indicates that the creators of MACHIAVELLI and Delphi “copy” ethical concepts and embed them in LLMs without questioning or challenging these concepts themselves sufficiently. If an ethical concept causes friction with the computer code, they only reduce and simplify the ethical concept in order to stay as close as possible to the original. We propose that ME should expand its focus to include both interdisciplinary efforts that embed existing ethical work into AI, and transdisciplinary research that fosters new interpretations of ethical concepts. Interdisciplinary and transdisciplinary approaches are crucial for creating AI systems that are not only effective but also socially responsible. To enhance collaboration between ethicists and computer scientists, we recommend the use of Socratic Dialogue as a methodological tool, promoting deeper understanding of key terms and more effective integration of ethics in AI development. (shrink)

Despite the widespread use of automated decision-making (ADM) systems, they are often developed without involving the public or those directly affected, leading to concerns about systematic biases that may perpetuate structural injustices. Existing formal fairness approaches primarily focus on statistical outcomes across demographic groups or individual fairness, yet these methods reveal ambiguities and limitations in addressing fairness comprehensively. This paper argues for a holistic approach to algorithmic fairness that integrates procedural fairness, considering both decision-making processes and their outcomes. Procedural fairness (...) emphasizes the importance of fair decision-making procedures, which aligns with theories of relational justice that stress the quality of social relations and power dynamics. We highlight the need for substantive procedural fairness to ensure better outcomes and address forward-looking responsibilities. Additionally, we propose leveraging Public Engagement, a core dimension within the well-established Responsible Research and Innovation framework, to enhance procedural fairness in ADM systems. Our contribution underscores the value of Public Engagement in fostering fairer ADM processes, thereby expanding the current focus beyond technical outcome-based approaches to encompass broader procedural considerations. (shrink)

This perspective paper critically examines value-laden challenges that emerge when using standards to support regulation in the field of artificial intelligence, particularly within the context of the AI Act. It presents a dilemma arising from the inherent vagueness and contestable nature of the AI Act’s requirements. The effective implementation of these requirements necessitates addressing hard normative questions that involve complex value judgments. These questions, such as determining the acceptability of risks or the appropriateness of accuracy levels, need to be addressed (...) in order to achieve compliance with the AI Act. However, this creates a dilemma: either the hard normative questions left open by the AI Act are addressed by the standards or they are addressed by the actors involved in the conformity assessment. This paper argues that the latter approach is more likely. Consequently, regulatory intermediaries such as notified bodies will be responsible for making critical value judgments while evaluating compliance with the AI Act’s value-laden requirements. This shift raises a series of concerns and implications that warrant further exploration. (shrink)

Digital sovereignty is a term increasingly used by academics and policymakers to describe efforts by states, private companies, and citizen groups to assert control over digital technologies. This descriptive conception of digital sovereignty is normatively deficient as it centres discussion on how power is being asserted rather than evaluating whether actions are legitimate. In this article, I argue that digital sovereignty should be understood as a normative concept that centres on authority (i.e., legitimate control). A normative approach to digital sovereignty (...) is beneficial as it supports critical discourse about the desirability of actors’ assertions of control. It is also more closely aligned with traditional definitions of sovereignty that are grounded in ideas of sovereign authority. To operationalise this normative approach to digital sovereignty and demonstrate the deficiencies of a descriptive approach, the role that “Big Tech” companies are playing in controlling artificial intelligence is considered from both perspectives. Through this case study, it is highlighted that Big Tech companies assert a high degree of control (i.e., descriptive digital sovereignty), but that they lack strong input legitimacy and have a questionable amount of output legitimacy. For this reason, it is argued that Big Tech companies should only be considered quasi-sovereigns over AI. (shrink)

Online privacy policies or terms and conditions ideally provide users with information about how their personal data are being used. The reality is that very few users read them: they are long, often hard to understand, and ubiquitous. The average internet user cannot realistically read and understand all aspects that apply to them and thus give informed consent to the companies who use their personal data. In this article, we provide a basic overview of a solution to the problem. We (...) suggest that software could allow users to delegate the consent process and consent could thus be automated. The article investigates the practical feasibility of this idea. After suggesting that it is feasible, we develop some normative issues that we believe should be addressed before automated consent is implemented. (shrink)

Coghlan and Cox (Between death and suffering: Resolving the gamer’s dilemma. Ethics and Information Technology) offer a new resolution to the Gamer’s Dilemma (Luck, The Gamer’s Dilemma. Ethics and Information Technology). They argue that, while it is fitting for a person committing virtual child molestation to feel self-repugnance, it is not fitting for a person committing virtual murder to feel the same, and the fittingness of this feeling indicates each act’s moral permissibility. The aim of this paper is to determine (...) whether this resolution – the repugnant resolution – successfully resolves the Gamer’s Dilemma. We argue that it does not. (shrink)

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could significantly (...) control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI. (shrink)

Deepfakes raise various concerns: risks of political destabilization, depictions of persons without consent and causing them harms, erosion of trust in video and audio as reliable sources of evidence, and more. These concerns have been the focus of recent work in the philosophical literature on deepfakes. However, there has been almost no sustained philosophical analysis of deepfakes from the perspective of concerns about honesty and dishonesty. That deepfakes are potentially deceptive is unsurprising and has been noted. But under what conditions (...) does the use of deepfakes fail to be honest? And which human agents, involved in one way or another in a deepfake, fail to be honest, and in what ways? If we are to understand better the morality of deepfakes, these questions need answering. Our first goal in this paper, therefore, is to offer an analysis of paradigmatic cases of deepfakes in light of the philosophy of honesty. While it is clear that many deepfakes are morally problematic, there has been a rising counter-chorus claiming that deepfakes are not essentially morally bad, since there might be uses of deepfakes that are not morally wrong, or even that are morally salutary, for instance, in education, entertainment, activism, and other areas. However, while there are reasons to think that deepfakes can supply or support moral goods, it is nevertheless possible that even these uses of deepfakes are dishonest. Our second goal in this paper, therefore, is to apply our analysis of deepfakes and honesty to the sorts of deepfakes hoped to be morally good or at least neutral. We conclude that, perhaps surprisingly, in many of these cases the use of deepfakes will be dishonest in some respects. Of course, there will be cases of deepfakes for which verdicts about honesty and moral permissibility do not line up. While we will sometimes suggest reasons why moral permissibility verdicts might diverge from honesty verdicts, we will not aim to settle matters of moral permissibility. (shrink)

Newly powerful large language models have burst onto the scene, with applications across a wide range of functions. We can now expect to encounter their outputs at rapidly increasing volumes and frequencies. Some commentators claim that large language models are bullshitting, generating convincing output without regard for the truth. If correct, that would make large language models distinctively dangerous discourse participants. Bullshitters not only undermine the norm of truthfulness (by saying false things) but the normative status of truth itself (by (...) treating it as entirely irrelevant). So, do large language models really bullshit? I argue that they can, in the sense of issuing propositional content in response to fact-seeking prompts, without having first assessed that content for truth or falsity. However, I further argue that they need not bullshit, given appropriate guardrails. So, just as with human speakers, the propensity for a large language model to bullshit depends on its own particular make-up. (shrink)

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a “lethal prejudice.” In this paper, we defend the value of interpretability (...) in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to—and perhaps even harm—patients. (shrink)

In this special issue, we focus on the connection between conceptual engineering and the philosophy of technology. Conceptual engineering is the enterprise of introducing, eliminating, or revising words and concepts. The philosophy of technology examines the nature and significance of technology. We investigate how technologies such as AI and genetic engineering (so-called “socially disruptive technologies”) disrupt our practices and concepts, and how conceptual engineering can address these disruptions. We also consider how conceptual engineering can enhance the practice of ethical design. (...) The issue features seven articles that discuss a range of topics, including trust in blockchain applications and the evolving concept of nature. These articles highlight that as technology changes the world and our concepts, conceptual engineering provides invaluable tools and frameworks to reflect on these changes and adapt accordingly. (shrink)

AI-driven journalism refers to various methods and tools for gathering, verifying, producing, and distributing news information. Their potential is to extend human capabilities and create new forms of augmented journalism. Although scholars agreed on the necessity to embed journalistic values in these systems to make AI systems accountable, less attention was paid to data quality, while the results’ accuracy and efficiency depend on high-quality data in any machine learning task. Assessing data quality in the context of AI-driven journalism requires a (...) broader and interdisciplinary approach, relying on the challenges of data quality in machine learning and the ethical challenges of using machine learning in journalism. To better identify these, we propose a data quality assessment framework to support the collection and pre-processing stages in machine learning. It relies on three of the core principles of ethical journalism—accuracy, fairness, and transparency—and participates in the shift from model-centric to data-centric AI, by focusing on data quality to reduce reliance on large datasets with errors, making data labelling consistent, and better integrating journalistic knowledge. (shrink)