Tag: Design

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(Featured) Artificial intelligence and the doctor–patient relationship expanding the paradigm of shared decision making

Artificial intelligence and the doctor–patient relationship expanding the paradigm of shared decision making

Giorgia Lorenzini et al. examine the evolving nature of the doctor-patient relationship in the context of integrating artificial intelligence (AI) into healthcare. They focus on the shared decision-making (SDM) process between doctors and patients, a consensual partnership founded on communication and respect for voluntary choices. The authors argue that the introduction of AI can potentially enhance SDM, provided it is implemented with care and consideration. The paper addresses the communication between doctors and AI and the communication of this interaction to patients, evaluating its potential impact on SDM and proposing strategies to preserve both doctors’ and patients’ autonomy.

The authors explore the communication and autonomy challenges arising from AI integration into clinical practice. They posit that AI’s influence could unintentionally limit doctors’ autonomy by heavily guiding their decisions, which in turn raises questions about the balance of power in the decision-making process. The paper emphasizes the importance of doctors understanding AI’s recommendations and checking for errors while also being competent in working with AI systems. By examining the “black box problem” of AI’s opaqueness, the authors argue that explainability is crucial for fostering the AI-doctor relationship and preserving doctors’ autonomy.

The paper then investigates doctor-patient communication and autonomy within the context of AI integration. The authors argue that in order to promote patients’ autonomy and encourage their participation in SDM, doctors must disclose and discuss AI’s involvement in the clinical evaluation process. They also contend that AI should consider patients’ preferences and unique situations, thus ensuring that their values are respected and that they are able to participate actively in the SDM process.

In relating the research to broader philosophical issues, the authors’ examination of the AI-doctor-patient relationship aligns with questions surrounding the ethical and moral implications of AI in society. As AI increasingly permeates various aspects of our lives, its impact on human autonomy, agency, and moral responsibility becomes a focal point for philosophical inquiry. The paper contributes to this discourse by delving into the specific context of healthcare and the evolving dynamics of the doctor-patient relationship, providing a microcosm for understanding the broader implications of AI integration in human decision-making processes.

As the authors outline the potential benefits and challenges of incorporating AI into the SDM process, future research could investigate the practical implementation of AI in various clinical settings, evaluating the effectiveness of AI-doctor collaboration in promoting SDM. Further research might also address the training and education necessary for medical professionals to adapt to AI integration, ensuring a seamless transition that optimizes patient care. Additionally, exploring methods for incorporating patients’ values into AI algorithms could provide a path to more personalized and autonomy-respecting AI-assisted healthcare.

Abstract

Artificial intelligence (AI) based clinical decision support systems (CDSS) are becoming ever more widespread in healthcare and could play an important role in diagnostic and treatment processes. For this reason, AI-based CDSS has an impact on the doctor–patient relationship, shaping their decisions with its suggestions. We may be on the verge of a paradigm shift, where the doctor–patient relationship is no longer a dual relationship, but a triad. This paper analyses the role of AI-based CDSS for shared decision-making to better comprehend its promises and associated ethical issues. Moreover, it investigates how certain AI implementations may instead foster the inappropriate paradigm of paternalism. Understanding how AI relates to doctors and influences doctor–patient communication is essential to promote more ethical medical practice. Both doctors’ and patients’ autonomy need to be considered in the light of AI.

Artificial intelligence and the doctor–patient relationship expanding the paradigm of shared decision making
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(Featured) Mobile health technology and empowerment

Mobile health technology and empowerment

Karola V. Kreitmair critically evaluates the notion of empowerment that has become pervasive in the discourse surrounding direct-to-consumer (DTC) mobile health technologies. The author argues that while these technologies claim to empower users by providing knowledge, enabling control, and fostering responsibility, the actual outcome is often not genuine empowerment but merely the perception of empowerment. This distinction has significant implications for individuals who might be seeking to affect behavior change and improve their health and well-being.

The paper meticulously breaks down the concept of empowerment into five key features: knowledgeability, control, responsibility, availability of good choices, and healthy desires. The author presents a thorough review of the evidence related to the efficacy, privacy, and security concerns surrounding the use of m-health technologies. They demonstrate that these technologies, while marketed as empowering tools, often fail to live up to their promises and, in some cases, even contribute to negative health outcomes or exacerbate existing issues such as disordered eating.

The core of the argument lies in the distinction between genuine empowerment and the mere perception of empowerment. The author posits that, rather than fostering true empowerment, DTC m-health technologies often create a psychological illusion of control and knowledgeability. This illusion can lead users to form unrealistic expectations and place undue burden on themselves to effect change when the necessary conditions for change are not met. This “empowerment paradox” ultimately calls into question the purported benefits of DTC m-health technologies and the societal narrative around personal responsibility and control over one’s health.

This paper’s findings resonate with broader philosophical discussions around individual autonomy, agency, and the role of technology in shaping our lives. The empowerment paradox highlights the complex interplay between the individual and the structural factors that shape health outcomes. It raises crucial questions about the ethical implications of profit-driven technologies and the responsibilities of technology developers, marketers, and users in navigating an increasingly technologically-driven healthcare landscape. The insights from this paper contribute to ongoing debates about the nature of empowerment and the limits of individual autonomy in an age where our lives are increasingly mediated by technology.

Future research should focus on the prevalence and consequences of the empowerment paradox in the context of DTC m-health technologies. A deeper understanding of how individuals make decisions around their health in the presence of perceived empowerment could inform the development of more effective and ethically responsible technologies. Additionally, examining the social and cultural factors that influence the marketing and adoption of these technologies may provide insight into how the industry can foster genuine empowerment, rather than perpetuating an illusion of control. Ultimately, a more nuanced understanding of the relationship between DTC m-health technologies and empowerment will pave the way for a more responsible and equitable approach to healthcare in the digital age.

Abstract

Mobile Health (m-health) technologies, such as wearables, apps, and smartwatches, are increasingly viewed as tools for improving health and well-being. In particular, such technologies are conceptualized as means for laypersons to master their own health, by becoming “engaged” and “empowered” “managers” of their bodies and minds. One notion that is especially prevalent in the discussions around m-health technology is that of empowerment. In this paper, I analyze the notion of empowerment at play in the m-health arena, identifying five elements that are required for empowerment. These are (1) knowledge, (2) control, (3) responsibility, (4) the availability of good choices, and (5) healthy desires. I argue that at least sometimes, these features are not present in the use of these technologies. I then argue that instead of empowerment, it is plausible that m-health technology merely facilitates a feeling of empowerment. I suggest this may be problematic, as it risks placing the burden of health and behavior change solely on the shoulders of individuals who may not be in a position to affect such change.

Mobile health technology and empowerment
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(Featured) Machine Ethics: Do Androids Dream of Being Good People?

Machine Ethics: Do Androids Dream of Being Good People?

Gonzalo Génova, Valentín Moreno, and M. Rosario González explore the possibility and limitations of teaching ethical behavior to artificial intelligence. The paper delves into two main approaches to teaching ethics to machines: explicit ethical programming and learning by imitation. It highlights the difficulties faced by each approach and discusses the implications and potential issues surrounding the application of machine learning to ethical issues.

The authors begin by examining explicit ethical programming, such as Asimov’s Three Laws, and discuss the challenges involved in foreseeing the consequences of an act, as well as the necessity of having an explicit goal for ethical behavior. The second approach, learning by imitation, involves machines observing the behavior of experts or a majority in order to emulate them. The paper also discusses the Moral Machine experiment by MIT, which aimed to teach machines to make moral decisions based on the preferences of the majority.

Despite the potential of machine learning techniques, the authors argue that both approaches fail to capture the essence of genuine ethical thinking in human beings. They emphasize that ethics is not about following a code of conduct or imitating the behavior of others, but rather about critical thinking and the formation of one’s own conscience. The paper concludes by questioning whether machines can truly learn ethics like humans do, suggesting that current methods of teaching ethics to machines are inadequate for capturing the complexity of human ethical life.

The research presented in the paper raises important philosophical questions about the nature of ethics and the role of machines in our ethical lives. It challenges the instrumentalist and reductionist approaches to ethics, which view ethical values as computable or reducible to a set of rules. By highlighting the limitations of these approaches, the paper invites us to reconsider the importance of value rationality and the recognition of the uniqueness and unrepeatable nature of human beings in ethical considerations.

In light of these findings, future research could explore alternative approaches to teaching ethics to machines that go beyond mere rule-following or imitation. This could involve the development of novel machine learning techniques that foster critical thinking and the ability to reason with values without reducing them to numbers. Additionally, interdisciplinary collaboration between philosophers, AI researchers, and ethicists could further enrich our understanding of the ethical dimensions of artificial intelligence and help to develop AI systems that not only do the right thing but also respect the complexity and richness of human ethical life.

Abstract

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating… and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of a set of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely “following a moral code”. In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society.

Machine Ethics: Do Androids Dream of Being Good People?
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(Featured) Trojan technology in the living room?

Trojan technology in the living room?

Franziska Sonnauer and Andreas Frewer explore the delicate balance between self-determination and external determination in the context of older adults using assistive technologies, particularly those incorporating artificial intelligence (AI). The authors introduce the concept of a “tipping point” to delineate the transition between self-determination and external determination, emphasizing the importance of considering the subjective experiences of older adults when employing such technologies. To this end, the authors adopt self-determination theory (SDT) as a theoretical framework to better understand the factors that may influence this tipping point.

The paper argues that the tipping point is intrapersonal and variable, suggesting that fulfilling the three basic psychological needs outlined in SDT—autonomy, competence, and relatedness—can potentially shift the tipping point towards self-determination. The authors propose various strategies to achieve this, such as providing alternatives for assistance in old age, promoting health technology literacy, and prioritizing social connectedness in technological development. They also emphasize the need to include older adults’ perspectives in decision-making processes, as understanding their subjective experiences is crucial to recognizing and respecting their autonomy.

Moreover, the authors call for future research to explore the tipping point and factors affecting its variability in different contexts, including assisted suicide, health deterioration, and the use of living wills and advance care planning. They contend that understanding the tipping point between self-determination and external determination may enable the development of targeted interventions that respect older adults’ autonomy and allow them to maintain self-determination for as long as possible.

In a broader philosophical context, this paper raises important ethical questions concerning the role of technology in shaping human agency, autonomy, and decision-making processes. It challenges us to reflect on the ethical implications of increasingly advanced assistive technologies and the potential consequences of their indiscriminate use. The issue of the tipping point resonates with broader debates on the nature of free will, the limits of self-determination, and the moral implications of human-machine interactions. As AI continues to become more integrated into our lives, the question of how to balance self-determination and external determination takes on greater urgency and complexity.

For future research, it would be valuable to explore the concept of the tipping point in different cultural contexts, as perceptions of autonomy and self-determination may vary across societies. Additionally, interdisciplinary approaches that combine insights from philosophy, psychology, and technology could shed light on the complex interplay between human values and AI-driven systems. Finally, empirical research investigating the experiences of older adults using assistive technologies would provide valuable data to help refine our understanding of the tipping point and inform the development of more ethically sound technologies that respect individual autonomy and promote well-being.

Abstract

Assistive technologies, including “smart” instruments and artificial intelligence (AI), are increasingly arriving in older adults’ living spaces. Various research has explored risks (“surveillance technology”) and potentials (“independent living”) to people’s self-determination from technology itself and from the increasing complexity of sociotechnical interactions. However, the point at which self-determination of the individual is overridden by external influences has not yet been sufficiently studied. This article aims to shed light on this point of transition and its implications.

Trojan technology in the living room?
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(Featured) The five tests: designing and evaluating AI according to indigenous Māori principles

The five tests: designing and evaluating AI according to indigenous Māori principles

Luke Munn provides a critical analysis of the current paradigms of artificial intelligence (AI) development and offer a framework for a decolonial AI. The author argues that existing AI paradigms reproduce and reinforce coloniality and its attendant inequalities. To overcome this, he proposes a framework based on Indigenous concepts from Aotearoa (New Zealand), which offers a distinct set of principles and priorities that challenge Western technocratic norms. The framework is centered on five tests that prioritize human dignity, communal integrity, and ecological sustainability. The author suggests that the application of these tests can guide the design and development of AI products in a way that is more inclusive, thoughtful, and attentive to life in its various forms.

The author identifies two distinct pathways for applying their framework. The first pathway is designing, which involves applying the principles and priorities of the Five Tests to the development of AI products that are currently in progress. This involves asking questions about how these products can respect the sacred, preserve or enhance life force, and reconcile negative impacts in acceptable ways. The author suggests that iterative versions of software can be developed by engaging genuinely with these questions and resolving them through code, architectures, interfaces, and affordances. The second pathway is decolonizing, which involves a deeper and more sustained confrontation with current AI regimes. This pathway involves challenging generic, universalizing frames, stressing the connection and interdependence of human and ecological well-being, and carefully considering potential impacts and developing ways to mitigate them or redress them to satisfy involved parties.

The author’s framework challenges current AI paradigms and practices by raising questions about what data-driven technology should be doing, how it can be designed in ways that are more inclusive, communal, and sustainable, and what values and norms should be used to judge the success of a particular technology. He suggests that these questions are epistemological, cultural and historical, and social in nature. And he argues that understanding and undoing systems of inequality that have been formalized and fossilized over time is a massive undertaking that demands a long-term project that prioritizes social justice.

In broader philosophical terms, this paper raises questions about the relationship between technology and power, the role of knowledge systems in shaping our understanding of the world, and the importance of Indigenous perspectives in challenging dominant paradigms. The authors’ framework challenges the Western-centric assumptions that underpin current AI paradigms and highlights the importance of recognizing and respecting diverse perspectives and ways of knowing.

Future research could explore the practical implications of the Five Tests framework and how it can be applied in different contexts. It could also examine the ways in which Indigenous perspectives can challenge dominant paradigms in other fields, such as philosophy, politics, and economics. Additionally, research could explore the potential for cross-cultural collaboration in the development of AI and other technologies, and how this collaboration can facilitate the recognition and respect of diverse perspectives and knowledge systems. Finally, research could explore the broader implications of the authors’ framework for the relationship between technology and power and the potential for decolonial approaches to reshape our understanding of the role of technology in society.

Abstract

As AI technologies are increasingly deployed in work, welfare, healthcare, and other domains, there is a growing realization not only of their power but of their problems. AI has the capacity to reinforce historical injustice, to amplify labor precarity, and to cement forms of racial and gendered inequality. An alternate set of values, paradigms, and priorities are urgently needed. How might we design and evaluate AI from an indigenous perspective? This article draws upon the five Tests developed by Māori scholar Sir Hirini Moko Mead. This framework, informed by Māori knowledge and concepts, provides a method for assessing contentious issues and developing a Māori position. This paper takes up these tests, considers how each test might be applied to data-driven systems, and provides a number of concrete examples. This intervention challenges the priorities that currently underpin contemporary AI technologies but also offers a rubric for designing and evaluating AI according to an indigenous knowledge system.

The five tests: designing and evaluating AI according to indigenous Māori principles