Integrating human support with chatbot-based behavior change interventions raises three challenges: (1) attuning the support to an individual’s state (e.g., motivation) for enhanced engagement, (2) limiting the use of the concerning human resources for enhanced efficiency, and (3) optimizing outcomes on ethical aspects (e.g., fairness). Therefore, we conducted a study in which 679 smokers and vapers had a 20% chance of receiving human feedback between five chatbot sessions. We find that having received feedback increases retention and effort spent on preparatory activities. However, analyzing a reinforcement learning (RL) model fit on the data shows there are also states where not providing feedback is better. Even this “standard” benefit-maximizing RL model is value-laden. It not only prioritizes people who would benefit most, but also those who are already doing well and want feedback. We show how four other ethical principles can be incorporated to favor other smoker subgroups, yet, interdependencies exist.

This paper examines the potential educational uses of chat-based large language models (LLMs), moving past initial hype and skepticism. Although LLM outputs often evoke fascination and resemble human writing, they are unpredictable and must be used with discernment. Several metaphors—like calculators, cars, and drunk tutors—highlight distinct models for student interactions with LLMs, which we explore in the paper. We suggest that LLMs hold a potential in students’ learning by fostering proleptic reasoning through scaffolding, i.e., presenting a technological accompaniment in anticipating and responding to potential objections to arguments. Here, the technical limitations of LLMs can be reframed as beneficial when fostering anticipatory reasoning. Whether their outputs are accurate or not, evaluating them stimulates learning. LLMs require students to critically engage, emphasizing analytical thinking over mere memorization. This interaction helps solidify knowledge. Additionally, we explore how engaging with LLMs can prepare students for constructive collective discussions and provide first steps in addressing epistemic injustices by highlighting potential research blind spots. Thus, while acknowledging the sociopolitical and ethical complexities of using LLMs in education, we suggest that when used in an informed way, they can promote critical thinking through anticipatory reasoning.

With psychiatry lagging behind other medical fields in terms of innovation in instruments and methods, AI provides it an opportunity to catch up. Advocates of digital phenotyping promise to provide an objective tool that detects symptoms by analysing data from personal devices. We argue that digital phenotyping requires a more reflexive and critical approach to its design and an alignment of the clinicians’ interests in generating relevant evidence with the needs of service users who seek tools to manage their condition. We propose a felt informatics approach, situating digital phenotyping design within the problem space of pragmatist aesthetics. Within this perspective, felt life becomes a central object and a site for digital phenotyping design. This paper reveals the ways diagnostic data mediates mental ill health experience, emphasises the cultivation of aesthetic sensibility as a fundamental element of digital phenotyping and includes design considerations for practitioners and researchers.

Large Language Models (LLMs) are expected to significantly impact various socio-technical systems, offering transformative possibilities for improved interaction between humans and technology. However, their integration poses complex challenges due to the intricate interplay between societal structures, human behaviour, and technological innovation. This research explores these multifaceted challenges, emphasising the need for a human-centered approach in integrating LLMs to ensure that technological advancements are aligned with ethical standards and societal needs. Utilizing a structured methodology comprising a workshop, literature analysis, and expert collaborations, the study uses a multi-dimensional human-centered AI framework to guide the responsible integration of LLMs. Key insights include the importance of inclusive data, considering unintended consequences, maintaining privacy, and respecting intellectual property rights. The paper identifies and advocates for principles like human-in-the-loop, continuous longitudinal studies, proactive awareness campaigns, and regular audits to develop LLMs that are ethically sound, adaptable, and effectively integrated into various socio-technical systems, thus addressing user needs and broader societal impacts. The paper also underlines the importance of collaboration among academia, industry, and policymakers to develop LLMs that are ethically aligned, socially beneficial, and adaptable to future societal needs. The findings offer valuable insights into the strategic integration of LLMs, advocating for a broader research perspective beyond industrial motivations to fully understand and leverage LLMs in socio-technical landscapes.

Our democratic systems have been challenged by the proliferation of artificial intelligence (AI) and its pervasive usage in our society. For instance, by analyzing individuals’ social media data, AI algorithms may develop detailed user profiles that capture individuals’ specific interests and susceptibilities. These profiles are leveraged to derive personalized propaganda, with the aim of influencing individuals toward specific political opinions. To address this challenge, the value of privacy can serve as a bridge, as having a sense of privacy can create space for people to reflect on their own political stance prior to making critical decisions, such as voting for an election. In this paper, we explore a novel approach by harnessing the potential of AI to enhance the privacy of social-media data. By leveraging adversarial machine learning, i.e., “AI versus AI,” we aim to fool AI-generated user profiles to help users hold a stake in resisting political profiling and preserve the deliberative nature of their political choices. More specifically, our approach probes the conceptual possibility of infusing people’s social media data with minor alterations that can disturb user profiling, thereby reducing the efficacy of the personalized influences generated by political actors. Our study delineates the boundary of ethical and practical implications associated with this ‘AI versus AI’ approach, highlighting the factors for the AI and ethics community to consider in facilitating deliberative decision-making toward democratic elections.

In Moral Hermeneutics and Technology: Making Moral Sense through Human-Technology-World Relations, Olya Kudina explores the role of technology in the way people arrive at their moral intuitions and choices and revise their moral commitments, a phenomenon she calls “moral hermeneutics.” This book considers technology as a mediator of human relations and questions the traditional anthropocentric view of morality. Drawing on the philosophical traditions of postphenomenology and pragmatism and empirical explorations from multiple case studies, Kudina shows how values co-evolve with the dynamic human-technology-world environment and even change in response to it. Consequently, Kudina presents morality as a dynamic practice of sense-making, where people, technologies, and the cultural setting all play an active role. This book explores the implications of such a technologically mediated moral hermeneutics for the informed use, design, and governance of technologies, while accounting for the intimate connection between values and technologies.

Practice and recent evidence show that state-of-the-art (SotA) automatic speech recognition (ASR) systems do not perform equally well for all speaker groups. Many factors can cause this bias against different speaker groups. This paper, for the first time, systematically quantifies and finds speech recognition bias against gender, age, regional accents and non-native accents, and investigates the origin of this bias by investigating bias cross-lingually (i.e., Dutch and Mandarin) and for two different SotA ASR architectures (a hybrid DNN-HMM and an attention based end-to-end (E2E) model) through a phoneme error analysis. The results show that only a fraction of the bias can be explained by pronunciation differences between speaker groups, and that in order to mitigate bias, language- and architecture specific solutions need to be found.

We propose a pragmatist account of value change that helps to understand how and why values sometimes change due to technological developments. Inspired by John Dewey’s writings on value, we propose to understand values as evaluative devices that carry over from earlier experiences and that are to some extent shared in society. We discuss the various functions that values fulfil in moral inquiry and propose a conceptual framework that helps to understand value change as the interaction between three manifestations of value distinguished by Dewey, i.e., “immediate value,” “values as the result of inquiry” and “generalized values.” We show how this framework helps to distinguish three types of value change: value dynamism, value adaptation, and value emergence, and we illustrate these with examples from the domain of technology. We argue that our account helps to better understand how technology may induce value change, namely through the creation of what Dewey calls indeterminate situations, and we show how our account can integrate several insights on (techno)moral change offered by other authors.

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care.

This paper focuses on two examples of the introduction and use of COVID-19 contact tracing apps in The Netherlands (CoronaMelder) and Belgium (Coronalert). It aims to offer a critical, sociotechnical perspective on tracing apps to understand how social, technical, and institutional dimensions form the ingredients for increasing surveillance. While it is still too early to gauge the implications of surveillance-related initiatives in the fight against COVID-19, the “technology theatre” put in place worldwide has already shown that very little can be done to prevent the deployment of technologies, even if their effectiveness is yet to be determined. The context-specific perspective outlined here offers insights into the interests of many different actors involved in the technology theatre, for instance, the corporate interest in sociotechnical frameworks (both apps rely on the Google/Apple exposure notifications application programming interface). At the same time, our approach seeks to go beyond dystopian narratives that do not consider important sociocultural dimensions, such as choices made during app development and implementation to mitigate potential negative impacts on privacy.

This paper explores the productive role that social network platforms such as Facebook, play in the practice of memory-making. While such platforms facilitate interaction across distance and time, they also solidify human self-expression and memory-making by systematically confronting the users with their digital past. By relying on the framework of postphenomenology, the analysis will scrutinize the mediating role of the Memories feature of Facebook, powered by recurring algorithmic scheduling and devoid of meaningful context. More specifically, it will show how this technological infrastructure mediates the concepts of memory, control and space, evoking a specific interpretation of the values of time, remembering and forgetting. As such, apart from preserving memories, Facebook appears as their co-producer, guiding the users in determining the criteria for remembering and forgetting. The paper finishes with suggestions on how to critically appropriate the memory-making features of social network platforms that would both enable their informed use and account for their mediating role in co-shaping good memories.

This paper explores the role of moral uncertainty in explaining the morally disruptive character of new technologies. We argue that existing accounts of technomoral change do not fully explain its disruptiveness. This explanatory gap can be bridged by examining the epistemic dimensions of technomoral change, focusing on moral uncertainty and inquiry. To develop this account, we examine three historical cases: the introduction of the early pregnancy test, the contraception pill, and brain death. The resulting account highlights what we call “differential disruption” and provides a resource for fields such as technology assessment, ethics of technology, and responsible innovation.

Purpose: This paper aims to show how the production of meaning is a matter of people interacting with technologies, throughout their appropriation and in co-performances. The researchers rely on the case of household-based voice assistants that endorse speaking as a primary mode of interaction with technologies. By analyzing the ethical significance of voice assistants as co-producers of moral meaning intervening in the material and socio-cultural space of the home, the paper invites their informed and critical use as a form of (re-)empowerment while acknowledging their productive role in human values. Design/methodology/approach: This paper presents an empirically informed philosophical analysis. Using the conceptual frameworks of technological appropriation and human–technological performances, while drawing on the interviews with voice assistants’ users and literature studies, this paper unravels the meaning-making processes in relation to these technologies in the household use. It additionally draws on a Wittgensteinian perspective to attend to the productive role of language and link to wider cultural meanings. Findings: By combining two approaches, appropriation and technoperformances, and analyzing the themes of privacy, power and knowledge, the paper shows how voice assistants help to shape a specific moral subject: embodied in space and made as it performatively responds to the device and makes sense of it together with others. Originality/value: The researchers show how through making sense of technologies in appropriation and performatively responding to them, people can change and intervene in the power structures that technologies suggest.

In this paper, I argue that AI-powered voice assistants, just as all technologies, actively mediate our interpretative structures, including values. I show this by explaining the productive role of technologies in the way people make sense of themselves and those around them. More specifically, I rely on the hermeneutics of Gadamer and the material hermeneutics of Ihde to develop a hermeneutic lemniscate as a principle of technologically mediated sense-making. The lemniscate principle links people, technologies and the sociocultural world in the joint production of meaning and explicates the feedback channels between the three counterparts. When people make sense of technologies, they necessarily engage their moral histories to comprehend new technologies and fit them in daily practices. As such, the lemniscate principle offers a chance to explore the moral dynamics taking place during technological appropriation. Using digital voice assistants as an example, I show how these AI-guided devices mediate our moral inclinations, decisions and even our values, while in parallel suggesting how to use and design them in an informed and critical way.

Rationale: This paper aims to show how the focus on eradicating bias from Machine Learning decision-support systems in medical diagnosis diverts attention from the hermeneutic nature of medical decision-making and the productive role of bias. We want to show how an introduction of Machine Learning systems alters the diagnostic process. Reviewing the negative conception of bias and incorporating the mediating role of Machine Learning systems in the medical diagnosis are essential for an encompassing, critical and informed medical decision-making. Methods: This paper presents a philosophical analysis, employing the conceptual frameworks of hermeneutics and technological mediation, while drawing on the case of Machine Learning algorithms assisting doctors in diagnosis. This paper unravels the non-neutral role of algorithms in the doctor's decision-making and points to the dialogical nature of interaction not only with the patients but also with the technologies that co-shape the diagnosis. Findings: Following the hermeneutical model of medical diagnosis, we review the notion of bias to show how it is an inalienable and productive part of diagnosis. We show how Machine Learning biases join the human ones to actively shape the diagnostic process, simultaneously expanding and narrowing medical attention, highlighting certain aspects, while disclosing others, thus mediating medical perceptions and actions. Based on that, we demonstrate how doctors can take Machine Learning systems on board for an enhanced medical diagnosis, while being aware of their non-neutral role. Conclusions: We show that Machine Learning systems join doctors and patients in co-designing a triad of medical diagnosis. We highlight that it is imperative to examine the hermeneutic role of the Machine Learning systems. Additionally, we suggest including not only the patient, but also colleagues to ensure an encompassing diagnostic process, to respect its inherently hermeneutic nature and to work productively with the existing human and machine biases.

This article explores the understanding of values in Responsible Research and Innovation (RRI). First, it analyses how two mainstream RRI approaches, the largely substantial one by Von Schomberg and the procedural one by Stilgoe and colleagues, identify and conceptualize values. We argue that by treating values as relatively stable entities, directly available for reflection, both fall into an ‘entity trap’. As a result, the hermeneutic work required to identify values is overlooked. We therefore seek to bolster a practice-based take on values, which approaches values as the evolving results of valuing processes. We highlight how this approach views values as lived realities, interactive and dynamic, discuss methodological implications for RRI, and explore potential limitations. Overall, the strength of this approach is that it enables RRI scholars and practitioners to better acknowledge the complexities involved in valuing.