The gamer’s dilemma offers three plausible but jointly inconsistent premises: Virtual murder in video games is morally permissible. Virtual paedophelia in video games is not morally permissible. There is no morally relevant difference between virtual murder and virtual paedophelia in video games. In this paper I argue that the gamer’s dilemma can be understood as one of three distinct dilemmas, depending on how we understand two key ideas in Morgan Luck’s original formulation. The two ideas are those of occurring in (...) a video game and being a virtual instance of murder or paedophelia. Depending on the weight placed on the gaming context, the dilemma is either about in-game acts or virtual acts. And depending on the type of virtual acts we have in mind, the dilemma is either about virtual representations or virtual partial reproductions of murder and paedophelia. This gives us three dilemmas worth resolving: a gaming dilemma, a representation dilemma, and a simulation dilemma. I argue that these dilemmas are about different issues, apply to different cases, and are susceptible to different solutions. I also consider how different participants in the debate have interpreted the dilemma in one or more of these three ways. (shrink)

There is a current debate about if, and in what sense, machine learning systems used in the medical context need to be explainable. Those arguing in favor contend these systems require post hoc explanations for each individual decision to increase trust and ensure accurate diagnoses. Those arguing against suggest the high accuracy and reliability of the systems is sufficient for providing epistemic justified beliefs without the need for explaining each individual decision. But, as we show, both solutions have limitations—and it (...) is unclear either address the epistemic worries of the medical professionals using these systems. We argue these systems do require an explanation, but an institutional explanation. These types of explanations provide the reasons why the medical professional should rely on the system in practice—that is, they focus on trying to address the epistemic concerns of those using the system in specific contexts and specific occasions. But ensuring that these institutional explanations are fit for purpose means ensuring the institutions designing and deploying these systems are transparent about the assumptions baked into the system. This requires coordination with experts and end-users concerning how it will function in the field, the metrics used to evaluate its accuracy, and the procedures for auditing the system to prevent biases and failures from going unaddressed. We contend this broader explanation is necessary for either post hoc explanations or accuracy scores to be epistemically meaningful to the medical professional, making it possible for them to rely on these systems as effective and useful tools in their practices. (shrink)

Algorithmic decision-making based on profiling may significantly affect people’s destinies. As a rule, however, explanations for such decisions are lacking. What are the chances for a “right to explanation” to be realized soon? After an exploration of the regulatory efforts that are currently pushing for such a right it is concluded that, at the moment, the GDPR stands out as the main force to be reckoned with. In cases of profiling, data subjects are granted the right to receive meaningful information (...) about the functionality of the system in use; for fully automated profiling decisions even an explanation has to be given. However, the trade secrets and intellectual property rights involved must be respected as well. These conflicting rights must be balanced against each other; what will be the outcome? Looking back to 1995, when a similar kind of balancing had been decreed in Europe concerning the right of access, Wachter et al. find that according to judicial opinion only generalities of the algorithm had to be disclosed, not specific details. This hardly augurs well for a future right of access let alone to explanation. Thereupon the landscape of IPRs for machine learning is analysed. Spurred by new USPTO guidelines that clarify when inventions are eligible to be patented, the number of patent applications in the US related to ML in general, and to “predictive analytics” in particular, has soared since 2010—and Europe has followed. I conjecture that in such a climate of intensified protection of intellectual property, companies may legitimately claim that the more their application combines several ML assets that, in addition, are useful in multiple sectors, the more value is at stake when confronted with a call for explanation by data subjects. Consequently, the right to explanation may be severely crippled. (shrink)

Rapid developments in Artificial Intelligence are leading to an increasing human reliance on machine decision making. Even in collaborative efforts with Decision Support Systems, where a human expert is expected to make the final decisions, it can be hard to keep the expert actively involved throughout the decision process. DSSs suggest their own solutions and thus invite passive decision making. To keep humans actively ‘on’ the decision-making loop and counter overreliance on machines, we propose a ‘reflection machine’. This system asks (...) users questions about their decision strategy and thereby prompts them to evaluate their own decisions critically. We discuss what forms RMs can take and present a proof-of-concept implementation of a RM that can produce feedback on users’ decisions in the medical and law domains. We show that the prototype requires very little domain knowledge to create reasonably intelligent critiquing questions. With this prototype, we demonstrate the technical feasibility to develop RMs and hope to pave the way for future research into their effectiveness and value. (shrink)

The increased presence of medical AI in clinical use raises the ethical question which standard of explainability is required for an acceptable and responsible implementation of AI-based applications in medical contexts. In this paper, we elaborate on the emerging debate surrounding the standards of explainability for medical AI. For this, we first distinguish several goods explainability is usually considered to contribute to the use of AI in general, and medical AI in specific. Second, we propose to understand the value of (...) explainability relative to other available norms of explainable decision-making. Third, in pointing out that we usually accept heuristics and uses of bounded rationality for medical decision-making by physicians, we argue that the explainability of medical decisions should not be measured against an idealized diagnostic process, but according to practical considerations. We conclude, fourth, to resolve the issue of explainability-standards by relocating the issue to the AI’s certifiability and interpretability. (shrink)

While rapid advances in artificial intelligence hiring tools promise to transform the workplace, these algorithms risk exacerbating existing biases against marginalized groups. In light of these ethical issues, AI vendors have sought to translate normative concepts such as fairness into measurable, mathematical criteria that can be optimized for. However, questions of disability and access often are omitted from these ongoing discussions about algorithmic bias. In this paper, I argue that the multiplicity of different kinds and intensities of people’s disabilities and (...) the fluid, contextual ways in which they manifest point to the limits of algorithmic fairness initiatives. In particular, existing de-biasing measures tend to flatten variance within and among disabled people and abstract away information in ways that reinforce pathologization. While fair machine learning methods can help mitigate certain disparities, I argue that fairness alone is insufficient to secure accessible, inclusive AI. I then outline a disability justice approach, which provides a framework for centering disabled people’s experiences and attending to the structures and norms that underpin algorithmic bias. (shrink)

This paper approaches the interaction of a health professional with an AI system for diagnostic purposes as a hybrid decision making process and conceptualizes epistemo-ethical constraints on this process. We argue for the importance of the understanding of the underlying machine epistemology in order to raise awareness of and facilitate realistic expectations from AI as a decision support system, both among healthcare professionals and the potential benefiters. Understanding the epistemic abilities and limitations of such systems is essential if we are (...) to integrate AI into the decision making processes in a way that takes into account its applicability boundaries. This will help to mitigate potential harm due to misjudgments and, as a result, to raise the trust—understood here as a belief in reliability of—in the AI system. We aim at a minimal requirement for AI meta-explanation which should distinguish machine epistemic processes from similar processes in human epistemology in order to avoid confusion and error in judgment and application. An informed approach to the integration of AI systems into the decision making for diagnostic purposes is crucial given its high impact on health and well-being of patients. (shrink)

This paper proposes a conceptual framework to study and evaluate the impact of ‘Algorithmic Management’ on worker dignity. While the literature on AM addresses many concerns that relate to the dignity of workers, a shared understanding of what worker dignity means, and a framework to study it, in the context of software algorithms at work is lacking. We advance a conceptual framework based on a Capability Approach as a route to understanding worker dignity under AM. This paper contributes to the (...) existing AM literature which currently is mainly focused on exploitation and violations of dignity and its protection. By using a CA, we expand this focus and can evaluate the possibility that AM might also enable and promote dignity. We conclude that our CA-based conceptual framework provides a valuable means to study AM and then discuss avenues for future research into the complex relationship between worker dignity and AM systems. (shrink)

Developers are often the engine behind the creation and implementation of new technologies, including in the artificial intelligence surge that is currently underway. In many cases these new technologies introduce significant risk to affected stakeholders; risks that can be reduced and mitigated by such a dominant party. This is fully recognized by texts that analyze risks in the current AI transformation, which suggest voluntary adoption of ethical standards and imposing ethical standards via regulation and oversight as tools to compel developers (...) to reduce such risks. However, what these texts usually sidestep is the question of how aware developers are to the risks they are creating with these new AI technologies, and what their attitudes are towards such risks. This paper asks to rectify this gap in research, by analyzing an ongoing case study. Focusing on six Israeli AI startups in the field of radiology, I carry out a content analysis of their online material in order to examine these companies’ stances towards the potential threat their automated tools pose to patient safety and to the work-standing of healthcare professionals. Results show that these developers are aware of the risks their AI products pose, but tend to deny their own role in the technological transformation and dismiss or downplay the risks to stakeholders. I conclude by tying these findings back to current risk-reduction recommendations with regards to advanced AI technologies, and suggest which of them hold more promise in light of developers’ attitudes. (shrink)

The internet presents not just opportunities but also risks that range, to name a few, from online abuse and misinformation to the polarisation of public debate. Given the increasingly digital nature of our societies, these risks make it essential for users to learn how to wisely use digital technologies as part of a more holistic approach to promoting human flourishing. However, insofar as they are exacerbated by both the affordances and the political economy of the internet, this article argues that (...) a new understanding of wisdom that is germane to the digital age is needed. As a result, we propose a framework for conceptualising what we call cyber-wisdom, and how this can be cultivated via formal education, in ways that are grounded in neo-Aristotelian virtue ethics and that build on three prominent existing models of wisdom. The framework, according to which cyber-wisdom is crucial to navigating online risks and opportunities through the deployment of character virtues necessary for flourishing online, suggests that cyber-wisdom consists of four components: cyber-wisdom literacy, cyber-wisdom reasoning, cyber-wisdom self-reflection, cyber-wisdom motivation. Unlike the models on which it builds, the framework accounts for the specificity of the digital age and is both conceptual and practical. On the one hand, each component has conceptual implications for what it means to be wise in the digital age. On the other hand, informed by character education literature and practice, it has practical implications for how to cultivate cyber-wisdom in the classroom through teaching methods that match its different components. (shrink)

Computational design uses artificial intelligence to optimise designs towards user-determined goals. When combined with 3D printing, it is possible to develop and construct physical products in a wide range of geometries and materials and encapsulating a range of functionality, with minimal input from human designers. One potential application is the development of bespoke surgical tools, whereby computational design optimises a tool’s morphology for a specific patient’s anatomy and the requirements of the surgical procedure to improve surgical outcomes. This emerging application (...) of AI and 3D printing provides an opportunity to examine whether new technologies affect the ethical responsibilities of those operating in high-consequence domains such as healthcare. This research draws on stakeholder interviews to identify how a range of different professions involved in the design, production, and adoption of computationally designed surgical tools, identify and attribute responsibility within the different stages of a computationally designed tool’s development and deployment. Those interviewed included surgeons and radiologists, fabricators experienced with 3D printing, computational designers, healthcare regulators, bioethicists, and patient advocates. Based on our findings, we identify additional responsibilities that surround the process of creating and using these tools. Additionally, the responsibilities of most professional stakeholders are not limited to individual stages of the tool design and deployment process, and the close collaboration between stakeholders at various stages of the process suggests that collective ethical responsibility may be appropriate in these cases. The role responsibilities of the stakeholders involved in developing the process to create computationally designed tools also change as the technology moves from research and development to approved use. (shrink)

In light of the widespread use of algorithmic systems across numerous domains, there is an increasing awareness about the need to explain their underlying decision-making process and resulting outcomes. Since oftentimes these systems are being considered as black boxes, adding explanations to their outcomes may contribute to the perception of their transparency and, as a result, increase users’ trust and fairness perception towards the system, regardless of its actual fairness, which can be measured using various fairness tests and measurements. Different (...) explanation styles may have a different impact on users’ perception of fairness towards the system and on their understanding of the outcome of the system. Hence, there is a need to understand how various explanation styles may impact non-expert users’ perceptions of fairness and understanding of the system’s outcome. In this study we aimed at fulfilling this need. We performed a between-subject user study in order to examine the effect of various explanation styles on users’ fairness perception and understanding of the outcome. In the experiment we examined four known styles of textual explanations along with a new style that reflect the results of an auditing process of the system. The results suggest that providing some kind of explanation contributes to users’ understanding of the outcome and that some explanation styles are more beneficial than others. Moreover, while explanations provided by the system are important and can indeed enhance users’ perception of fairness, their perception mainly depends on the outcome of the system. The results may shed light on one of the main problems in explainability of algorithmic systems, which is choosing the best explanation to promote users’ fairness perception towards a particular system, with respect to the outcome of the system. The contribution of this study is reflected in the new and realistic case study that was examined, in the creation and evaluation of a new explanation style that can be used as the link between the actual fairness of the system and users’ fairness perception and in the need of analyzing and evaluating explanations while taking into account the outcome of the system. (shrink)

This paper explores ethical debates associated with the UK COVID-19 contact tracing app that occurred in the public news media and broader public policy, and in doing so, takes ethics debate as an object for sociological study. The research question was: how did UK national newspaper news articles and grey literature frame the ethical issues about the app, and how did stakeholders associated with the development and/or governance of the app reflect on this? We examined the predominance of different ethical (...) issues in news articles and grey literature, and triangulated this using stakeholder interview data. Findings illustrate how news articles exceptionalised ethical debate around the app compared to the way they portrayed ethical issues relating to ‘manual’ contact tracing. They also narrowed the debate around specific privacy concerns. This was reflected in the grey literature, and interviewees perceived this to have emerged from a ‘privacy lobby’. We discuss the findings, and argue that this limited public ethics narrative masked broader ethical issues. (shrink)

In recent years, increasingly advanced artificial intelligence, and in particular machine learning, has shown great promise as a tool in various healthcare contexts. Yet as machine learning in medicine has become more useful and more widely adopted, concerns have arisen about the “black-box” nature of some of these AI models, or the inability to understand—and explain—the inner workings of the technology. Some critics argue that AI algorithms must be explainable to be responsibly used in the clinical encounter, while supporters of (...) AI dismiss the importance of explainability and instead highlight the many benefits the application of this technology could have for medicine. However, this dichotomy fails to consider the particular ways in which machine learning technologies mediate relations in the clinical encounter, and in doing so, makes explainability more of a problem than it actually is. We argue that postphenomenology is a highly useful theoretical lens through which to examine black-box AI, because it helps us better understand the particular mediating effects this type of technology brings to clinical encounters and moves beyond the explainability stalemate. Using a postphenomenological approach, we argue that explainability is more of a concern for physicians than it is for patients, and that a lack of explainability does not introduce a novel concern to the physician–patient encounter. Explainability is just one feature of technological mediation and need not be the central concern on which the use of black-box AI hinges. (shrink)

The importance of fairness in machine learning models is widely acknowledged, and ongoing academic debate revolves around how to determine the appropriate fairness definition, and how to tackle the trade-off between fairness and model performance. In this paper we argue that besides these concerns, there can be ethical implications behind seemingly purely technical choices in fairness interventions in a typical model development pipeline. As an example we show that the technical choice between in-processing and post-processing is not necessarily value-free and (...) may have serious implications in terms of who will be affected by the specific fairness intervention. The paper reveals how assessing the technical choices in terms of their ethical consequences can contribute to the design of fair models and to the related societal discussions. (shrink)

This paper sets out an account of trust in AI as a relationship between clinicians, AI applications, and AI practitioners in which AI is given discretionary authority over medical questions by clinicians. Compared to other accounts in recent literature, this account more adequately explains the normative commitments created by practitioners when inviting clinicians’ trust in AI. To avoid committing to an account of trust in AI applications themselves, I sketch a reductive view on which discretionary authority is exercised by AI (...) practitioners through the vehicle of an AI application. I conclude with four critical questions based on the discretionary account to determine if trust in particular AI applications is sound, and a brief discussion of the possibility that the main roles of the physician could be replaced by AI. (shrink)

This article analyzes emerging artificial intelligence -enhanced lie detection systems from ethical and human resource management perspectives. I show how these AI enhancements transform lie detection, followed with analyses as to how the changes can lead to moral problems. Specifically, I examine how these applications of AI introduce human rights issues of fairness, mental privacy, and bias and outline the implications of these changes for HR management. The changes that AI is making to lie detection are altering the roles of (...) human test administrators and human subjects, adding machine learning-based AI agents to the situation and establishing invasive data collection processes as well as introducing certain biases in results. I project that the potentials for pervasive and continuous lie detection initiatives are substantial, displacing human-centered efforts to establish trust and foster integrity in organizations. I argue that if it is possible for HR managers to do so, they should cease using technologically-based lie detection systems entirely and work to foster trust and accountability on a human scale. However, if these AI-enhanced technologies are put into place by organizations by law, agency mandate, or other compulsory measures, care should be taken that the impacts of the technologies on human rights and wellbeing are considered. The article explores how AI can displace the human agent in some aspects of lie detection and credibility assessment scenarios, expanding the prospects for inscrutable, “black box” processes and novel physiological constructs that may increase the potential for such human rights concerns as fairness, mental privacy, and bias. Employee interactions with autonomous lie detection systems rather with than human beings who administer specific tests can reframe organizational processes and rules concerning the assessment of personal honesty and integrity. The dystopian projection of organizational life in which analyses and judgments of the honesty of one’s utterances are made automatically and in conjunction with one’s personal profile provides unsettling prospects for the autonomy of self-representation. (shrink)

The introduction of automated vehicles promises an increase in traffic safety. Prior to its launch proof of the anticipated reduction in the sense of a positive risk balance compared with human driving performance is required from various stakeholders such as the European Union Commission, the German Ethic Commission, and the ISO TR 4804. To meet this requirement and to generate acceptance by the public and the regulatory authorities, a qualitative Risk- Benefit framework has been defined. This framework is based on (...) literature research on approaches applied in other disciplines. This report depicts the framework, adapted from the pharmaceutical sector called PROACT-URL which serves as a structured procedure to demonstrate a positive risk balance in an understandable and transparent manner. The qualitative framework needs to be turned in quantitative methods once it should be applied. Therefore, two steps of the framework are discussed in more detail: First, the definition of adequate development thresholds that are required at an early stage of the development. Second the simulation-based assessment to prove the positive risk balance prior to the market introduction. (shrink)

Explainable artificial intelligence is an emerging, multidisciplinary field of research that seeks to develop methods and tools for making AI systems more explainable or interpretable. XAI researchers increasingly recognise explainability as a context-, audience- and purpose-sensitive phenomenon, rather than a single well-defined property that can be directly measured and optimised. However, since there is currently no overarching definition of explainability, this poses a risk of miscommunication between the many different researchers within this multidisciplinary space. This is the problem we seek (...) to address in this paper. We outline a framework, called Explanatory Pragmatism, which we argue has two attractive features. First, it allows us to conceptualise explainability in explicitly context-, audience- and purpose-relative terms, while retaining a unified underlying definition of explainability. Second, it makes visible any normative disagreements that may underpin conflicting claims about explainability regarding the purposes for which explanations are sought. Third, it allows us to distinguish several dimensions of AI explainability. We illustrate this framework by applying it to a case study involving a machine learning model for predicting whether patients suffering disorders of consciousness were likely to recover consciousness. (shrink)

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. Here, we propose a novel way of tackling the value alignment problem as a two-step process. The first step consists on formalising moral values and value aligned behaviour based on philosophical foundations. Our formalisation is compatible with the framework of Reinforcement Learning, to ease the handling of an agent’s individual and ethical objectives. The second step consists in designing an environment (...) wherein an agent learns to behave ethically while pursuing its individual objective. We leverage on our theoretical results to introduce an algorithm that automates our two-step approach. In the cases where value-aligned behaviour is possible, our algorithm produces a learning environment for the agent wherein it will learn a value-aligned behaviour. (shrink)

Different people have different perceptions about artificial intelligence. It is extremely important to bring together all the alternative frames of thinking—from the various communities of developers, researchers, business leaders, policymakers, and citizens—to properly start acknowledging AI. This article highlights the ‘fruitful collaboration’ that sociology and AI could develop in both social and technical terms. We discuss how biases and unfairness are among the major challenges to be addressed in such a sociotechnical perspective. First, as intelligent machines reveal their nature of (...) ‘magnifying glasses’ in the automation of existing inequalities, we show how the AI technical community is calling for transparency and explainability, accountability and contestability. Not to be considered as panaceas, they all contribute to ensuring human control in novel practices that include requirement, design and development methodologies for a fairer AI. Second, we elaborate on the mounting attention for technological narratives as technology is recognized as a social practice within a specific institutional context. Not only do narratives reflect organizing visions for society, but they also are a tangible sign of the traditional lines of social, economic, and political inequalities. We conclude with a call for a diverse approach within the AI community and a richer knowledge about narratives as they help in better addressing future technical developments, public debate, and policy. AI practice is interdisciplinary by nature and it will benefit from a socio-technical perspective. (shrink)

Fairness is one of the most prominent values in the Ethics and Artificial Intelligence debate and, specifically, in the discussion on algorithmic decision-making. However, while the need for fairness in ADM is widely acknowledged, the very concept of fairness has not been sufficiently explored so far. Our paper aims to fill this gap and claims that an ethically informed re-definition of fairness is needed to adequately investigate fairness in ADM. To achieve our goal, after an introductory section aimed at clarifying (...) the aim and structure of the paper, in section “Fairness in algorithmic decision-making” we provide an overview of the state of the art of the discussion on fairness in ADM and show its shortcomings; in section “Fairness as an ethical value”, we pursue an ethical inquiry into the concept of fairness, drawing insights from accounts of fairness developed in moral philosophy, and define fairness as an ethical value. In particular, we argue that fairness is articulated in a distributive and socio-relational dimension; it comprises three main components: fair equality of opportunity, equal right to justification, and fair equality of relationship; these components are grounded in the need to respect persons both as persons and as particular individuals. In section “Fairness in algorithmic decision-making revised”, we analyze the implications of our redefinition of fairness as an ethical value on the discussion of fairness in ADM and show that each component of fairness has profound effects on the criteria that ADM ought to meet. Finally, in section “Concluding remarks”, we sketch some broader implications and conclude. (shrink)

In this study, I use the Critical Realism perspective of power to explain how the Bitcoin protocol operates as a system of power. I trace the ideological underpinnings of the protocol in the Cypherpunk movement to consider how notions of power shaped the protocol. The protocol by design encompasses structures, namely Proof of Work and Trustlessness that reproduce asymmetrical constraints on the entities that comprise it. These constraining structures generate constraining mechanisms, those of cost effectiveness and deanonymisation, which further restrict (...) participating entities’ ‘power to act’, reinforcing others’ ‘power over’ them. In doing so, I illustrate that the Bitcoin protocol, rather than decentralising and distributing power across a network of numerous anonymous, trustless peers, it has instead shifted it, from the traditional actors to newly emergent ones. (shrink)