Autonomy is a core value that is deeply entrenched in the moral, legal, and political practices of many societies. The development and deployment of artificial intelligence (AI) have raised new questions about AI’s impacts on human autonomy. However, systematic assessments of these impacts are still rare and often held on a case-by-case basis. In this article, I provide a conceptual framework that both ties together seemingly disjoint issues about human autonomy, as well as highlights differences between them. In the first (...) part, I distinguish between distinct concerns that are currently addressed under the umbrella term ‘human autonomy’. In particular, I show how differentiating between autonomy-as-authenticity and autonomy-as-agency helps us to pinpoint separate challenges from AI deployment. Some of these challenges are already well-known (e.g. online manipulation or limitation of freedom), whereas others have received much less attention (e.g. adaptive preference formation). In the second part, I address the different roles AI systems can assume in the context of autonomy. In particular, I differentiate between AI systems taking on agential roles and AI systems being used as tools. I conclude that while there is no ‘silver bullet’ to address concerns about human autonomy, considering its various dimensions can help us to systematically address the associated risks. (shrink)

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Some claim that digital phenotyping will revolutionize understanding of human psychology and experience and significantly promote human wellbeing. This paper investigates the nature of digital phenotyping in relation to its alleged promise. Unlike most of the literature to date on philosophy and digital phenotyping, which has focused on its ethical aspects, this paper focuses on its epistemic and methodological aspects. The paper advances a tetra-taxonomy involving four scenario types in which knowledge may be acquired from human “digitypes” by digital phenotyping. (...) These scenarios comprise two causal relations and a correlative and constitutive relation that can exist between information generated by digital systems/devices on the one hand and psychological or behavioral phenomena on the other. The paper describes several modes of inference involved in deriving knowledge within these scenarios. After this epistemic mapping, the paper analyzes the possible knowledge potential and limitations of digital phenotyping. It finds that digital phenotyping holds promise of delivering insight into conditions and states as well producing potentially new psychological categories. It also argues that care must be taken that digital phenotyping does not make unwarranted conclusions and is aware of potentially distorting effects in digital sensing and measurement. If digital phenotyping is to truly revolutionize knowledge of human life, it must deliver on a range of fronts, including making accurate forecasts and diagnoses of states and behaviors, providing causal explanations of these phenomena, and revealing important constituents of human conditions, psychology, and experience. (shrink)

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Global lockdowns during the COVID-19 pandemic have offered many people first-hand experience of how their daily online activities threaten their digital well-being. This article begins by critically evaluating the current approaches to digital well-being offered by ethicists of technology, NGOs, and social media corporations. My aim is to explain why digital well-being needs to be reimagined within a new conceptual paradigm. After this, I lay the foundations for such an alternative approach, one that shows how current digital well-being initiatives can (...) be designed in more insightful ways. This new conceptual framework aims to transform how philosophers of technology think about this topic, as well as offering social media corporations practical ways to design their technologies in ways that will improve the digital well-being of users. (shrink)

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This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. The (...) imperatives of the “participatory turn” in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (shrink)

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Existing ethical guidelines that aim to guide the development of mental health apps tend to overemphasize the role of Western conceptual frameworks. While such frameworks have proved to be a useful first step in introducing ethics to a previously unregulated industry, the rapid global uptake of mental health apps requires thinking more deeply about the diverse populations these apps seek to serve. One way to do this is to introduce more intercultural ethical perspectives into app design and the guidelines that (...) aim to encourage best practices. In addition to this, existing ethical guidelines can also benefit from the ethical scholarship from the feminist and disability traditions, both of which highlight specific ethical considerations for vulnerable users. Rethinking the ethical responsibilities of mental health app designers through the prisms of feminism, disability studies, and intercultural philosophy leads us to a more global and inclusive set of ethical considerations in app design. This white paper explores what existing guidelines for the regulation of mental health apps are missing. It also explores how these guidelines could be improved for users who inhabit an increasingly diverse and globalized world. (shrink)

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The adoption of AI in the health sector has its share of benefits and harms to various stakeholder groups and entities. There are critical risks involved in using AI systems in the health domain; risks that can have severe, irreversible, and life-changing impacts on people’s lives. With the development of innovative AI-based applications in the medical and healthcare sectors, new types of risks emerge. To benefit from novel AI applications in this domain, the risks need to be managed in order (...) to protect the fundamental interests and rights of those affected. This will increase the level to which these systems become ethically acceptable, legally permissible, and socially sustainable. In this paper, we first discuss the necessity of AI risk management in the health domain from the ethical, legal, and societal perspectives. We then present a taxonomy of risks associated with the use of AI systems in the health domain called HART. HART mirrors the risks of a variety of different real-world incidents caused by use of AI in the health sector. Lastly, we discuss the implications of the taxonomy for different stakeholder groups and further research. (shrink)

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