In traditional research on the relationship between human and technology, agency has always been a concept that has attracted much attention. Starting from Aristotle, agency is considered as an agent’s initiative in action. Since Giddens defined agency as the power of change, material agency has emerged.

Clinical decision-making is being supported by machine learning models. So-called decision-support systems (DSS) are intended to improve the decision-making of human operators (e.g., physicians), but they also introduce issues of epistemic uncertainty and over-reliance, and thereby open up responsibility gaps. To overcome these shortcomings, explainable AI attempts to provide insights into how the system made a decision. Explanations are, however, provided post-hoc and require contextual interpretation.

Why is there an ethics of X for some X but not others? There is a thriving academic research program of medical ethics, but not of the ethics of travel or of fashion. In this paper, I explore this question and apply it to AI and other information technologies. Is there a distinctive ethics of AI or ML or can they be subsumed under a broader type of applied ethics of technology? Recently, Heilinger 2022 has suggested that thinking there is a distinctive ethics of AI is as misguided as thinking there is a distinctive ethics of electricity.

This paper introduces a novel theoretical approach to understanding artefact functions by advocating for a "normative functions" account, inspired by the literature on conceptual functions in philosophy. Normative functions of concepts are, roughly, things that they allow us to do that matter normatively (for example, things in virtue of which we have normative reasons to have these concepts).

Social media algorithms severely limit our interactions and exposure to other perspectives. However, individuals could still have a responsibility regarding their engagement online. Current literature focuses mostly on echo chambers, algorithmic control, and misinformation. Echo chambers are defined by manipulation whereas epistemic bubbles can be innocently or accidentally exclusive. Instead of algorithms, I consider the role people play in creating social media epistemic bubbles (SMEBs) and the consequential harms on one’s epistemic norms and status as a knower.

In recent years ‘Responsible AI’ (R-AI) has been applied to a number of contexts and research applications (Dignum, 2019; Zhu, 2019; De Laat, 2021). On the surface this seems a good thing, as of course we want the development, deployment, and use of AI-systems to be in line with certain normative principles, and it seems the ‘responsible’ frame can give us just that. R-AI can ensure that AI-systems respect human rights and are aligned with democratic values. However, just what exactly R-AI means is contested, and often undefined. 

In the field of digital ethics, the question has been asked regularly whether digital ethics is unique in the kinds of ethical issues it raises, moral principles it requires or methods or approaches it is in need of. A debate on this issue took place between 1985 and 2002, and has been titled the uniqueness debate within digital ethics (or computer ethics, at the time). Various authors, such as Deborah Jpohnson, Walter Maner, Krystyna Gorniak-Kocikowska and Luciano Floridi, made arguments in favor of uniqueness.

AI is developing rapidly, as are concerns about the impact of its training and deployment on the environment. Recent studies suggest that since 2019, data centers have produced more CO2-emissions than the aviation industry (Shift Project, 2019), and they are extremely water-demanding (Li et al., 2023a). Given the urgency to achieve AI growth sustainably, the environmental impact of AI itself can no longer be overlooked. While studies about the environmental impact of AI have begun to emerge in the past few years, this emergent knowledge brings about new ethical questions and dilemmas.

One of the big shifts in healthcare caused by so-called disruptive innovations in healthcare powered by AI and big data, is a shift from diagnostic and curative healthcare to predictive and preventive healthcare. While preventive healthcare is almost exclusively cloaked in positive attributes, we need to maintain semantic clarity about what it can and cannot deliver, so that patients are not misguided about its benefits and limitations and can make well-informed decisions regarding their healthcare.

The integration of digital twin technology in agriculture presents a promise for enhancing animal welfare, particularly within livestock management. This article explores some ethical implications of employing digital twins in agriculture, guided by the established Five Freedoms for Animal Welfare framework.

Bioethics traditionally focuses on normative questions related to medical practice. The inquiry involves the moral permissibility of using new technologies for medical purpose and also existing medical technologies for non-medical purpose. While these inquiries probe into the ethical issues raised by the medical technologies, they take a rather reactive attitude towards the application of technologies.

Socially disruptive technologies have been described as technologies that disturb or significantly alter social practices. In terms of the social practice of ethical evaluation, new technologies can lead to misapplication or blurring of philosophical concepts. Consequently, ethics' function of practical guidance may be compromised, creating moral uncertainty.

This paper utilizes Derrida’s philosophy of tele-technology to examine how social media shapes perceptions of religious violence. Although Derrida did not witness the advent of social media, he already saw how the widespread ownership of television and the accessibility of portable cameras in the late 50s and 60s marked a turning point in our ‘access’ to events all over the world. Today, social media has intensified this access further, as the digitization of our lifeworld provides unparalleled access to free information.

Technological artefacts have become “world objects” – they affect the world as a whole. This becomes increasingly evident considering the imprint of their development and use on our global natural environment. The growing awareness of the entanglement of humans, their practices, and their technological artefacts with their natural environments goes along with growing uncertainty. The rising number of experiences with the catastrophic consequences of climate change and environmental crises are increasingly shaking many people’s belief in a stable course of life and of the future.

Innovations that have been regarded as disruptive in the medical realm, such as mHealth applications, or machine learning, are perceived as part of a positive shift towards a more preventive, participatory and affordable healthcare model. More recently, several contributions have started exploring the ecological impacts of disruptive innovations in healthcare. New principles have been developed concerning sustainable development and use of technology in health care.

The theme of technological breakdown, error, failure, or malfunction has, in a certain way, always been present in the history of the philosophy of technology. From Heidegger’s hammer to contemporary discussion of algorithmic bias, technological failure has been seen as revealing of something, as a means to obtain knowledge about technology or the world. For example, Verbeek (2004, p.

Agriculture is undergoing a great transformation, often pronounced the fourth agricultural revolution, driven by technologies such as robotics, variable rate chemical applicators, the Internet of Things, big data, drones and automation (Balafoutis et al. 2020). This transformation is marked by the double pressure of a burgeoning world population, on the one hand, and evermore strained life-support systems, on the other (Blok 2017, 133). Life-support systems include both wild ecosystems and human food production systems. Protecting wild ecosystems is a demanding imperative.

How to understand new encounters between the living and the technological? Exemplary for such new encounters are the biotechnological creations of synthetic biology, where life and technology are related in increasingly complicated and intimate ways. This developing biotechnological field frames its new entities as ‘artificial life’, ‘living technology’, and ‘biohybrid systems’.

AI is being developed for many applications in the livestock industry to control the environmental impact of livestock and reduce inefficiency in the sector while also responding to some of the ethical concerns raised earlier about the welfare of farm animals (Bao & Xie, 2022).

In Henry Shue’s influential 1999 article “Global Environment and International Inequality,” he argues not only that developed nations bear a disproportionately large burden of the costs involved in fixing the environmental problems caused by industrialization and globalization, but also that members of developing nations are morally permitted to cause environmental degradation insofar as they have been unfairly prevented from reaching the appropriate threshold of dignity and respect.

A high-level virtue ethics approach to situations of deep uncertainty would complement and/or contrast with consequentialist and deontological approaches to uncertainty. Such an account would satisfy the following criteria: (1) it provides normative guidance that allows individuals and societies to cope with deep uncertainty ethically and sustainably in the presence of strong emotions of fear, apprehension, and anxiety; (2) it allows for responsiveness to unexpected situations (“black swans” (Taleb 2007)); and (3) it is realistically accessible to ordinary people. 

Are social media companies responsible for disinformation? Studies of how algorithms prioritize information suggest that social media significantly contributes to the spread of disinformation. However, the theoretical resources for making sense of responsibility for spreading falsehoods are limited. A testimonial view of responsibility holds that information conduits like Facebook are not responsible. On this view, platforms are merely sources of information, and the proper locus of responsibility is rather with originators (Fricker, 2012).