Science would not be possible without trust among experts, trust of the public in experts, and reliance on scientific instruments and methods. The rapid adoption of scientific foundation models and their use in AI agents is changing scientific practices and thereby impacting this epistemic fabric which hinges on trust and reliance. Foundation models are machine learning models that are trained on large bodies of data and can be applied to a multitude of tasks. Their application in science raises the question of whether scientific foundation models can be relied upon as a research tool and to what extent, or even be trusted as if they were research partners.

Conceptual clarification of the notions of trust and reliance in science is pivotal in the face of foundation models. Trust and reliance form the glue for the increasingly distributed epistemic labour within contemporary technoscientific systems. We build on two concepts of trust in science, namely trust in science as shared values, and trust in science based on commitments to processes that provide objective claims. We analyse whether scientific foundation models are research tools to which the concept of reliance applies, or research partners that can be trustworthy or not. We consider these foundation models within their socio-technical contexts.

Allocation of trust should be reserved for human agents and the organizations they operate in. Reliance applies to foundation models and artificial intelligence agents. This distinction is important to unambiguously allocate responsibility, which is crucial in maintaining the fabric of trust that underpins science.@en

The advances in machine learning (ML)-based systems in medicine give rise to pressing epistemological and ethical questions. Clinical decisions are increasingly taken in highly digitised work environments, which we call artificial epistemic niches. By considering the case of ML systems in life-critical healthcare settings, we investigate (1) when users’ reliance on these systems can be characterised as epistemic dependence and (2) how this dependence turns into what we refer to as harmful epistemic dependence of clinical professionals on medical ML. The latter occurs when the impossibility of critically assessing the soundness of a system’s output in situ implies a moral obligation to comply with its recommendation since a failure to do so constitutes a moral risk that cannot be justified then and there. We analyse the epistemic and moral consequences of harmful epistemic dependence on the status of medical professionals. We conclude by assessing how a suitable design of the epistemic niche can address the problem.

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AI holds the potential to transform healthcare, promising improvements in patient care. Yet, realizing this potential is hampered by over-reliance on limited datasets and a lack of transparency in validation processes. To overcome these obstacles, we advocate the creation of a detailed registry for AI algorithms. This registry would document the development, training, and validation of AI models, ensuring scientific integrity and transparency. Additionally, it would serve as a platform for peer review and ethical oversight. By bridging the gap between scientific validation and regulatory approval, such as by the FDA, we aim to enhance the integrity and trustworthiness of AI applications in healthcare.

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This handbook presents the concept of ‘meaningful human control’ (MHC) over AI systems from the perspectives of (i) philosophy and ethics, (ii) law and governance, and (iii) design and engineering. The introductory chapter addresses the motivations and recent developments in MHC, introducing each perspective and related chapters. These three disciplinary perspectives scrutinize how MHC intertwines with philosophical debates on moral responsibility, societal concerns regarding control over technological advancements in legal frameworks, and the engineering complexities of designing and developing AI systems while ensuring human control and responsibility. Additionally, cross-cutting aspects on MHC over AI systems are also introduced and discussed through (iv) interdisciplinary and systemic perspectives. By offering a contextualized introduction to the perspectives considered in this handbook, this chapter aims to present the handbook’s various approaches and points of interest for a diverse audience, highlighting potential entry points into this multidisciplinary volume.

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Politicians and engineers are increasingly realizing that values are important in the development of technological artefacts. What is often overlooked is that different conceptualizations of these abstract values lead to different design-requirements. For example, designing social media platforms for deliberative democracy sets us up for technical work on completely different types of architectures and mechanisms than designing for so-called liquid or direct forms of democracy. Thinking about Democracy is not enough, we need to design for the proper conceptualization of these values. As we see it, we cannot responsibly engineer and innovate and shape technology in accordance with our moral values without engaging in systematic and continuous conceptual engineering: This is not only an academic, or theoretical issue, it is also not simply an issue for public policy or politics, or regulators, it has become a central problem for engineering and the world of technology. In this paper, we present a framework for doing the necessary conceptual work in the context of requirement engineering. We draw on the literature on conceptual engineering to lay out a methodology to (1) assess different conceptions and (2) to develop new conceptions. Moreover, we integrate this methodology with extant approaches in the philosophy of technology which aim at designing technological artefacts ethically. In the final section we apply this integrated framework to freedom in the context of social media networks.@en

In this paper, we examine the epistemological and ontological assumptions algorithmic hiring assessments make about job seekers’ attributes (e.g., competencies, skills, abilities) and the ethical implications of these assumptions. Given that both traditional psychometric hiring assessments and algorithmic assessments share a common set of underlying assumptions from the psychometric paradigm, we turn to literature that has examined the merits and limitations of these assumptions, gathering insights across multiple disciplines and several decades. Our exploration leads us to conclude that algorithmic hiring assessments are incompatible with attributes whose meanings are context-dependent and socially constructed. Such attributes call instead for assessment paradigms that offer space for negotiation of meanings between the job seeker and the employer. We argue that in addition to questioning the validity of algorithmic hiring assessments, this raises an often overlooked ethical impact on job seekers’ autonomy over self-representation: their ability to directly represent their identity, lived experiences, and aspirations. Infringement on this autonomy constitutes an infringement on job seekers’ dignity. We suggest beginning to address these issues through epistemological and ethical reflection regarding the choice of assessment paradigm, the means to implement it, and the ethical impacts of these choices. This entails a transdisciplinary effort that would involve job seekers, hiring managers, recruiters, and other professionals and researchers. Combined with a socio-technical design perspective, this may help generate new ideas regarding appropriate roles for human-to-human and human–technology interactions in the hiring process.@en

Governments are increasingly using sophisticated self-learning algorithms to automate and standardize decision-making on a large scale. However, despite aspirations for predictive data and more efficient decision-making, the introduction of artificial intelligence (AI) also gives rise to risks and creates a potential for harm. The attribution of responsibility to individuals for the harm caused by these novel socio-Technical decision-making systems is epistemically and normatively challenging. The conditions necessary for individuals to be adequately held responsible-moral agency, freedom, control, and knowledge, can be undermined by the introduction of algorithmic decision-making. Thereby responsibility gaps are created where seemingly no one is sufficiently responsible for the system's outcome. We turn this challenge to adequately attribute responsibility into a design challenge to design for these responsibility conditions. Drawing on philosophical responsibility literature, we develop a conceptual framework to scrutinize the task responsibilities of actors involved in the (re-)design and application of algorithmic decision-making systems. This framework is applied to an empirical case study involving AI in automated governmental decision-making. We find that the framework enables the critical assessment of a socio-Technical system's design for responsibility and provides valuable insights to prevent future harm. The article addresses the current academic and empirical lack of philosophical insights to understand and design for responsibilities in novel algorithmic ICT systems. @en

We propose methods for an AI agent to estimate the value preferences of individuals in a hybrid participatory system, considering a setting where participants make choices and provide textual motivations for those choices. We focus on situations where there is a conflict between participants' choices and motivations, and operationalize the philosophical stance that 'valuing is deliberatively consequential.' That is, if a user's choice is based on a deliberation of value preferences, the value preferences can be observed in the motivation the user provides for the choice. Thus, we prioritize the value preferences estimated from motivations over the value preferences estimated from choices alone. We evaluate the proposed methods on a dataset of a large-scale survey on energy transition. The results show that explicitly addressing inconsistencies between choices and motivations improves the estimation of an individual's value preferences. The proposed methods can be integrated in a hybrid participatory system, where artificial agents ought to estimate humans' value preferences to pursue value alignment. @en

Supported by the arrival of 5G and, soon 6G, digital technologies are evolving towards an artificial intelligence-driven internet of robotic and bionano things. The merging of artificial intelligence (AI) with other technologies such as the internet of things (IoT) gives rise to acronyms such as 'AIoT', 'IoRT' (IoT and robotics) and 'IoBNT' (IoT and bionano technology). Blockchain, augmented reality and virtual reality add even more technological options to the mix. Smart bodies, smart homes, smart industries, smart cities and smart governments lie ahead, with the promise of many benefits and opportunities. However, unprecedented amounts of personal data will be collected, and digital technologies will affect the most intimate aspects of our life more than ever, including in the realms of love and friendship. This study offers a bird's eye perspective of the key societal and ethical challenges we can expect as a result of this convergence, and policy options that can be considered to address them effectively.@en

The costs of the COVID-19 pandemic are yet to be calculated, but they include the loss of millions of lives and the destruction of countless livelihoods. What is certain is that the SARS-CoV-2 virus has changed the way we live for the foreseeable future. It has forced many to live in ways they would have previously thought impossible. As well as challenging scientists and medical professionals to address urgent value conflicts in the short term, COVID-19 has raised slower-burning value questions for corporations, public institutions, governments, and policymakers. In simple terms, the pandemic has brought what we care about into sharp relief, both collectively and individually. Whether this revaluation of our values will last beyond the current pandemic is unknown. Once COVID-19 has been tamed, will the desire to return to our previous lives be irresistible? Or will living under pandemic conditions have taught us something that will be incorporated into how we design our future lives and technologies? These are hard questions for the ethics of technology, which this volume aims to explore and address.@en

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.@en

The digital revolution has brought about many societal changes such as the creation of “smart cities”. The smart city concept has changed the urban ecosystem by embedding digital technologies in the city fabric to enhance the quality of life of its inhabitants. However, it has also led to some pressing issues and challenges related to data, privacy, ethics inclusion, and fairness. While the initial concept of smart cities was largely technology-and data-driven, focused on the automation of traffic, logistics and processes, this concept is currently being replaced by technology-enabled, human-centred solutions. However, this is not the end of the development, as there is now a big trend towards “design for values”. In this paper, we point out how a value-sensitive design approach could promote a more sustainable pathway of cities that better serves people and nature. Such “valuesensitive design” will have to take ethics, law and culture on board. We discuss how organising the digital world in a participatory way, as well as leveraging the concepts of self-organisation, selfregulation, and self-control, would foster synergy effects and thereby help to leverage a sustainable technological revolution on a global scale. Furthermore, a “democracy by design” approach could also promote resilience.

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In this article, we report on eight grand challenges for value sensitive design, which were developed at a one-week workshop, Value Sensitive Design: Charting the Next Decade, Lorentz Center, Leiden, The Netherlands, November 14–18, 2016. A grand challenge is a substantial problem, opportunity, or question that motives sustained research and design activity. The eight grand challenges are: (1) Accounting for Power, (2) Evaluating Value Sensitive Design, (3) Framing and Prioritizing Values, (4) Professional and Industry Appropriation, (5) Tech policy, (6) Values and Human Emotions, (7) Value Sensitive Design and Intelligent Algorithms, and (8) Value Tensions. Each grand challenge consists of a discussion of its importance and a set of tractable key questions.

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The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

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In this article, we introduce the Special Issue, Value Sensitive Design: Charting the Next Decade, which arose from a week-long workshop hosted by Lorentz Center, Leiden, The Netherlands, November 14–18, 2016. Forty-one researchers and designers, ranging in seniority from doctoral students to full professors, from Australia, Europe, and North America, and representing a wide range of academic fields participated in the workshop. The first article in the special issue puts forward eight grand challenges for value sensitive design to help guide and shape the field. It is followed by 16 articles consisting of value sensitive design nuggets—short pieces of writing on a new idea, method, challenge, application, or other concept that engages some aspect of value sensitive design. The nuggets are grouped into three clusters: theory, method, and applications. Taken together the grand challenges and nuggets point the way forward for value sensitive design into the next decade and beyond.

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The article ‘‘An ethico-legal framework for social data science’’, written by Nikolaus Forgó, Stefanie Hänold, Jeroen van den Hoven, Tina Krügel, Iryna Lishchuk, René Mahieu, Anna Monreale, Dino Pedreschi, Francesca Pratesi, David van Putten originally published electronically on the publisher’s internet portal (currently SpringerLink) on April 10, 2021 without open access. The copyright of the article changed to@en