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(Featured) Ethics of AI and Health Care: Towards a Substantive Human Rights Framework

Ethics of AI and Health Care: Towards a Substantive Human Rights Framework

S. Matthew Liao provides an incisive exploration into the ethical considerations intrinsic to the application of artificial intelligence (AI) in healthcare contexts. The paper underscores the burgeoning interest in employing AI for health-related purposes, with AI applications demonstrating competencies in diagnosing certain types of cancer, identifying heart rhythm abnormalities, diagnosing various eye diseases, and even identifying viable embryos. However, the author cautions that the deployment of AI in healthcare settings necessitates adherence to robust ethical frameworks and guidelines.

The author identifies a burgeoning multitude of ethical frameworks for AI that have been proposed over recent years. The count of such frameworks exceeds 80 and stems from a diverse array of sources including private corporations, governmental agencies, academic institutions, and intergovernmental bodies. These frameworks commonly reference the four principles of biomedical ethics: autonomy, beneficence, non-maleficence, and justice, and often include recommendations for transparency, explainability, and trust. However, the author warns that the proliferation of these frameworks has led to confusion, thereby raising pressing questions about the basis, justification, and practical implementation of these recommendations.

In response to this conundrum, the author proposes an AI ethics framework rooted in substantive human rights theory. This proposed framework seeks to address the questions raised by the proliferation of ethical guidelines and to provide clear and practical guidance for the use of AI in healthcare. The author argues for an ethical framework that is not only abstract but also expounds the grounds and justifications of the recommendations it puts forward, as well as how these recommendations should be applied in practice.

The broader philosophical discourse that this research engages with is the ethics of technology and, more specifically, the ethical and moral implications of AI use in healthcare. The central philosophical question the author grapples with is the tension between the rapid development and application of AI in healthcare and the need for substantive ethical guidelines to govern its use. This brings into sharp focus the perennial philosophical tension between progress and ethical constraint, raising the specter of issues such as the nature of autonomy, the definition of harm, and the equitable distribution of benefits and burdens.

For future research, the author’s proposition of a human rights-based ethical framework opens up multiple avenues. First, the application of this framework could be examined in real-world healthcare scenarios to assess its efficacy in guiding ethical AI use. Second, the interplay between this framework and existing legal systems could be studied to ascertain any gaps or overlaps. Lastly, a comparative analysis could be conducted of how this proposed framework fares against other ethical frameworks in use, and how it might be refined or integrated with other approaches for a more robust ethical guidance in healthcare AI applications.

Abstract

There is enormous interest in using artificial intelligence (AI) in health care contexts. But before AI can be used in such settings, we need to make sure that AI researchers and organizations follow appropriate ethical frameworks and guidelines when developing these technologies. In recent years, a great number of ethical frameworks for AI have been proposed. However, these frameworks have tended to be abstract and not explain what grounds and justifies their recommendations and how one should use these recommendations in practice. In this paper, I propose an AI ethics framework that is grounded in substantive, human rights theory and one that can help us address these questions.

Ethics of AI and Health Care: Towards a Substantive Human Rights Framework
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(Featured) The black box problem revisited. Real and imaginary challenges for automated legal decision making

The black box problem revisited. Real and imaginary challenges for automated legal decision making

Bartosz Brożek et al. explore the ethical and practical dilemmas arising from the integration of Artificial Intelligence (AI) in the realm of law. The authors suggest that despite the perceived opacity and unpredictability of AI, these machines can provide rational and justifiable decisions in legal reasoning. By challenging conventional notions of decision-making and justifiability, the paper reframes the discussion around AI’s role in law and provides a compelling argument for AI’s potential to aid in legal reasoning.

The authors delve into the intricacies of legal decision-making, highlighting the contrast between our traditional expectations and the realities of legal reasoning. They argue that while we expect legal decisions to be based on clearly identifiable structures, algorithmic operations on beliefs, and classical logic, the cognitive science research paints a contrasting picture. The authors further suggest that most legal decisions emerge unconsciously, lack a recognizable structure, and are often influenced by emotional reactions and social training. This observation paves the way for a paradigm shift, suggesting that rather than the process, it is the justifiability of the decision ex post that is paramount.

The authors propose a two-module AI system, one intuitive and the other rational. The intuitive module, powered by machine learning, recognizes patterns from large datasets and makes decisions. The rational module, grounded in logic, does not make decisions but justifies those made by the intuitive module. In this framework, AI can be seen as rational if an acceptable justification can be provided for its decisions, despite their unpredictability. This interesting intertwining of machine learning and logic reshapes our understanding of AI’s role in legal decision-making.

This paper touches upon broader philosophical issues surrounding consciousness, rationality, and decision-making. By arguing for a shift from a process-oriented to a result-oriented evaluation of decision-making, the authors challenge the traditional Kantian perspective. The proposed model, in which an AI’s decisions are assessed based on their post-hoc justifiability, aligns more closely with consequentialist philosophy. This emphasis on the end result rather than the means to reach it further stimulates the ongoing debate on the ethical implications of AI use and the re-evaluation of long-held philosophical tenets in the face of technological advancements.

Future research could explore various facets of this proposed two-module AI system, particularly the interplay and potential conflicts between the intuitive and rational modules. Questions around what constitutes an “acceptable justification” in various legal contexts also demand further exploration. Additionally, research could investigate how this approach to AI in law would intersect with other legal principles, such as fairness, transparency, and due process. Ultimately, the paper presents a compelling case for rethinking the role and evaluation of AI in legal decision-making, opening up intriguing possibilities for future philosophical and legal discourse.

Abstract

This paper addresses the black-box problem in artificial intelligence (AI), and the related problem of explainability of AI in the legal context. We argue, first, that the black box problem is, in fact, a superficial one as it results from an overlap of four different – albeit interconnected – issues: the opacity problem, the strangeness problem, the unpredictability problem, and the justification problem. Thus, we propose a framework for discussing both the black box problem and the explainability of AI. We argue further that contrary to often defended claims the opacity issue is not a genuine problem. We also dismiss the justification problem. Further, we describe the tensions involved in the strangeness and unpredictability problems and suggest some ways to alleviate them.

The black box problem revisited. Real and imaginary challenges for automated legal decision making
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(Featured) Bare statistical evidence and the legitimacy of software-based judicial decisions

Bare statistical evidence and the legitimacy of software-based judicial decisions

Eva Schmidt et al. explore the question of whether evidence provided by software systems can serve as a legitimate basis for judicial decisions, focusing on two primary cases: recidivism predictions and DNA cold hit cases. The authors approach this question by analyzing the nature of bare statistical evidence and its relation to individualized evidence. They argue that while bare statistical evidence is generally considered insufficient to meet the standard of proof in criminal and civil cases, software-generated evidence can be individualized and, thus, meet this standard of proof under certain conditions.

In the case of recidivism predictions, the authors discuss the use of software systems such as COMPAS, which rely on bare statistical evidence to estimate the risk of an individual reoffending. They argue that for a sentence to be just and have the potential to serve as an incentive, it must be based on the specific features of the individual concerned, rather than solely on general features of a group that they belong to, which may correlate with high recidivism risk. The authors maintain that bare statistical evidence alone is insufficient for sentencing decisions.

Regarding DNA cold hit cases, the authors propose that statistical evidence generated by software systems like TrueAllele can be individualized through abductive reasoning or inference to the best explanation when it comes to cases of extreme probability. They argue that when the best explanation for the evidence is that the defendant is the source of the crime scene DNA, the evidence can be considered individualized and, thus, meet the standard of proof for criminal cases. This aligns with the normic account of individualized evidence, which posits that the best explanation of a piece of evidence is also the most normal explanation.

The authors’ analysis raises broader philosophical questions concerning the nature of evidence, the role of statistical reasoning in judicial decision-making, and the ethical implications of using software systems in the courtroom. It highlights the importance of distinguishing between different types of support, such as abductive (normic) support and probabilistic support, and of understanding the connections and disconnections between these concepts. Moreover, the paper touches on issues related to transparency, explainability, and fairness in the use of software systems as decision-making aids in the legal context.

Future research could further explore the implications of using software systems for other types of legal evidence and decision-making processes, as well as the ethical and epistemological challenges that these systems pose. Additionally, investigating the relationship between individualized evidence and statistical reasoning could shed light on the nature of evidence itself and the standards of proof required in various legal contexts. Finally, future work could focus on the development of guidelines and best practices for the implementation and evaluation of software systems in the courtroom, addressing issues such as transparency, explainability, and the appropriate weighting of statistical and individualized evidence.

Abstract

Can the evidence provided by software systems meet the standard of proof for civil or criminal cases, and is it individualized evidence? Or, to the contrary, do software systems exclusively provide bare statistical evidence? In this paper, we argue that there are cases in which evidence in the form of probabilities computed by software systems is not bare statistical evidence, and is thus able to meet the standard of proof. First, based on the case of State v. Loomis, we investigate recidivism predictions provided by software systems used in the courtroom. Here, we raise problems for software systems that provide predictions that are based on bare statistical evidence. Second, by examining the case of People v. Chubbs, we argue that the statistical evidence provided by software systems in cold hit DNA cases may in some cases suffice for individualized evidence, on a view on which individualized evidence is evidence that normically supports the relevant proposition (Smith, in Mind 127:1193–1218, 2018).

Bare statistical evidence and the legitimacy of software-based judicial decisions
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(Featured) Algorithmic Nudging: The Need for an Interdisciplinary Oversight

Algorithmic Nudging: The Need for an Interdisciplinary Oversight

Christian Schmauder et al. critically assess the implications and risks of employing “black box” AI systems for the development and implementation of personalized nudges in various domains of life. They begin by outlining the power and promise of algorithmic nudging, drawing attention to how AI-driven nudges could bring about widespread benefits in areas such as health, finance, and sustainability. However, they contend that outsourcing nudging to opaque AI systems poses challenges in terms of understanding the underlying reasons for their effectiveness and addressing potential unintended consequences.

The authors delve deeper into the nuances of algorithmic nudging by examining the role of personalized advice in influencing human decision-making. They highlight a key concern that arises when AI systems attempt to maximize user satisfaction: the tendency of the algorithms to exploit cognitive biases in order to achieve desired outcomes. Consequently, the effectiveness of the AI-developed nudges might come at the cost of truthfulness, ultimately undermining the very goals they were designed to achieve.

To address this issue, the authors advocate for the need to look “under the hood” of AI systems, arguing that understanding the underlying cognitive processes harnessed by these systems is crucial for mitigating unintended side effects. They emphasize the importance of interdisciplinary collaboration between computer scientists, cognitive scientists, and psychologists in the development, monitoring, and refinement of AI systems designed to influence human decision-making.

The authors’ exploration of the limitations and risks of “black box” AI nudges raises broader philosophical concerns, particularly in relation to the ethics of autonomy, transparency, and accountability. These concerns call into question the balance between leveraging AI-driven nudges to benefit society and preserving individual autonomy and freedom of choice. Furthermore, the analysis highlights the tension between relying on AI’s predictive power and fostering a deeper understanding of the mechanisms driving human behavior.

This paper provides a valuable foundation for future research on the ethical and philosophical implications of AI-driven nudging. Further investigation could delve into the possible approaches to designing more transparent and explainable AI systems, exploring how such systems might enhance, rather than hinder, human decision-making processes. Additionally, researchers could examine the moral responsibilities of AI developers and regulators, studying the ethical frameworks necessary to guide the development and deployment of AI nudges that respect human autonomy, values, and dignity. Ultimately, a deeper understanding of these complex philosophical questions will be instrumental in realizing the full potential of AI-driven nudges while safeguarding against their potential pitfalls.

Abstract

Nudge is a popular public policy tool that harnesses well-known biases in human judgement to subtly guide people’s decisions, often to improve their choices or to achieve some socially desirable outcome. Thanks to recent developments in artificial intelligence (AI) methods new possibilities emerge of how and when our decisions can be nudged. On the one hand, algorithmically personalized nudges have the potential to vastly improve human daily lives. On the other hand, blindly outsourcing the development and implementation of nudges to “black box” AI systems means that the ultimate reasons for why such nudges work, that is, the underlying human cognitive processes that they harness, will often be unknown. In this paper, we unpack this concern by considering a series of examples and case studies that demonstrate how AI systems can learn to harness biases in human judgment to reach a specified goal. Drawing on an analogy in a philosophical debate concerning the methodology of economics, we call for the need of an interdisciplinary oversight of AI systems that are tasked and deployed to nudge human behaviours.

Algorithmic Nudging: The Need for an Interdisciplinary Oversight
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(Featured) Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Frank Ursin et al. investigate the ethical considerations associated with medical artificial intelligence (AI), particularly in the context of radiology. They emphasize the importance of implementing explainable AI (XAI) techniques to address epistemic and explanatory concerns that arise when AI is employed in medical decision-making. The authors outline a four-level approach to explicability, comprising disclosure, intelligibility, interpretability, and explainability, with each successive level representing an escalation in the level of detail and clarity provided to the patient or physician.

The authors argue that XAI has great potential in the medical field, and they present two examples from radiology to illustrate its practical applications. The first example involves the use of image inpainting techniques to generate sharper and more detailed saliency maps, which can help localize relevant regions within radiological images. The second example highlights the importance of natural language communication in XAI, where an image-to-text model is used to generate medical reports based on radiological images. These two examples demonstrate that incorporating XAI techniques in radiology can provide valuable insights and improved communication for medical practitioners and patients.

In the paper’s conclusion, the authors emphasize the need for a tailored approach to explicability that considers the needs of patients and the scope of medical decisions. They also advocate for the use of insights gained from medical AI ethics to re-evaluate established medical practices and confront biases in medical classification systems. By applying the four levels of explicability in a thoughtful manner, the authors posit that ethically defensible information processes can be established when utilizing medical AI.

This paper touches on broader philosophical issues related to the ethics of technology, medical autonomy, and the nature of trust in AI-driven decision-making. As AI becomes increasingly integrated into various domains of human activity, questions about transparency, fairness, and the moral implications of AI systems become paramount. This paper demonstrates the necessity of establishing an ethical framework for AI applications in healthcare, providing valuable insights that can be extended to other disciplines as well. By considering the complex interplay between AI-driven systems and human agents, the authors also underscore the importance of understanding how technological advancements impact the broader social fabric and the values we uphold as a society.

Future research in this area could explore the generalizability of the four-level approach to explicability in other medical domains or even non-medical contexts. Additionally, researchers may investigate how the incorporation of diverse perspectives in the development of AI systems and explainability techniques can mitigate the potential for biases and discriminatory outcomes. It would also be valuable to study how XAI can be adapted to the specific needs and preferences of individual patients or physicians, creating personalized approaches to explicability. Lastly, researchers may wish to assess the long-term impact of integrating XAI in medical practice, particularly in terms of patient satisfaction, physician trust, and overall quality of care.

Abstract

Definition of the problem

The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI?

Arguments

We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example.

Conclusion

We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements.

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?
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(Featured) A phenomenological perspective on AI ethical failures: The case of facial recognition technology

A phenomenological perspective on AI ethical failures: The case of facial recognition technology

Yuni Wen and Matthias Holweg conduct a philosophical analysis of the responses of four prominent technology firms to the ethical concerns surrounding the use and development of facial recognition technology. The article meticulously delves into the controversies surrounding Amazon, IBM, Microsoft, and Google, as they grapple with public backlash and stakeholder disapproval. By analyzing these cases, the authors elucidate four distinct strategies that these organizations employ to mitigate potential reputation loss: deflection, improvement, validation, and pre-emption. They astutely highlight the spectrum of these responses, ranging from the most accommodative to the most defensive approach.

The authors propose three possible antecedents that may determine an organization’s response strategy to controversial AI technology: the financial importance of the technology to the company, the strategic importance of the technology to the company’s product and service offerings, and the degree to which the controversial technology violates the company’s stated public values. Through their examination of the facial recognition controversies and the strategies employed by the tech giants, they provide invaluable insights into how these factors contribute to shaping the responses of companies facing ethical dilemmas in AI technology.

Although the article’s primary focus is on large technology firms, it acknowledges the limitations of its analysis and encourages further research on small and medium-sized firms, non-profit organizations, public sector organizations, and other entities that may intentionally misuse AI for nefarious purposes. It also highlights the need for future research to consider the interplay between organizational strategies and the varying global regulatory landscape concerning AI technology, given the diverse policy initiatives and regional differences.

The article not only contributes to the ongoing discourse about AI ethics but also resonates with broader philosophical debates on corporate social responsibility and the role of organizations in shaping a just and equitable society. In an era of unprecedented technological advances and heightened awareness of ethical concerns, this research raises pertinent questions about the duties and responsibilities that companies bear in addressing the potential social and moral implications of their products and services. It underscores the challenge that organizations face in balancing financial interests and strategic goals with ethical imperatives and societal expectations.

To enrich our understanding of the complex interplay between organizations and AI ethics, future research could explore the processes through which companies develop and implement their response strategies, with an emphasis on the role of leadership, organizational culture, and internal and external stakeholder dynamics. Moreover, investigating how these strategies evolve over time and assessing their effectiveness in addressing public concerns could provide valuable insights into best practices for organizations navigating the ethical minefield of AI technology. Ultimately, this line of inquiry would contribute significantly to our understanding of how corporations can foster the responsible development and use of AI, ensuring that its potential benefits are realized while mitigating its ethical risks.

Abstract

As more and more companies adopt artificial intelligence to increase the efficiency and effectiveness of their products and services, they expose themselves to ethical crises and potentially damaging public controversy associated with its use. Despite the prevalence of AI ethical problems, most companies are strategically unprepared to respond effectively to the public. This paper aims to advance our empirical understanding of company responses to AI ethical crises by focusing on the rise and fall of facial recognition technology. Specifically, through a comparative case study of how four big technology companies responded to public outcry over their facial recognition programs, we not only demonstrated the unfolding and consequences of public controversies over this new technology, but also identified and described four major types of company responses—Deflection, Improvement, Validation, and Pre-emption. These findings pave the way for future research on the management of controversial technology and the ethics of AI.

A phenomenological perspective on AI ethical failures: The case of facial recognition technology
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(Featured) Merging Minds: The Conceptual and Ethical Impacts of Emerging Technologies for Collective Minds

Merging Minds: The Conceptual and Ethical Impacts of Emerging Technologies for Collective Minds

David M. Lyreskog et al. outline and analyze the ethical implications and conceptual challenges surrounding technologically enabled collective minds (TCMs). The paper proposes four main categories to help understand the varying levels of unity and directionality in TCMs: DigiMinds, UniMinds, NetMinds, and MacroMinds. Each category has its own set of unique ethical challenges, which the authors argue should be considered in a multidimensional manner to effectively address the complexities of agency and responsibility in TCMs.

DigiMinds are minimally direct, minimally directional interfaces, such as virtual avatars in digital spaces, where individuals are separate but can communicate through digital means. UniMinds are low-directional, highly direct interfaces, in which senders can communicate and manipulate neuronal behavior in receivers. This category is further divided into Weak UniMinds, which are collaborative interfaces, and Strong UniMinds, which create an entirely new joint entity. NetMinds, on the other hand, are minimally direct, highly directional tools that facilitate vast networks of collective thinking, such as swarm intelligence applications. Lastly, MacroMinds are maximally direct and maximally directed tools, with multiple participants connected through interfaces that allow direct neuronal transmissions in all directions. This category is also subdivided into Weak MacroMinds, which are collaborative interfaces, and Strong MacroMinds, which create new joint entities.

The authors argue that each of these four categories challenges our current understanding of collective and joint actions, urging a reevaluation of the conceptual and ethical frameworks that guide our thinking. For instance, UniMinds and MacroMinds raise questions about identity, agency, and responsibility when a new entity emerges from the connected individuals. In NetMinds, the role of the computer as an organizer poses challenges concerning responsibility and transparency. The paper suggests that instead of a binary approach, future ethical analyses should consider the technological specifications, the domain in which the TCM is deployed, and the reversibility of joining a Collective Mind.

This research taps into broader philosophical issues surrounding the nature of identity, consciousness, and agency in an increasingly interconnected world. As we move towards a future where technology not only extends our cognitive capabilities but also has the potential to fundamentally reshape our understanding of what it means to be an individual, we are forced to reevaluate our traditional conceptions of personhood, ethics, and responsibility. TCMs challenge the philosophical foundations of agency and responsibility, as well as the ways in which we understand and define collective versus individual actions and decisions.

To further explore the ethical and conceptual challenges of TCMs, future research could delve deeper into the practical implications of integrating these technologies into various aspects of our society, such as healthcare, education, governance, and commerce. Avenues for research might include examining the legal and policy ramifications of TCMs, the potential for power imbalances in such systems, and the implications for privacy and autonomy. Additionally, scholars could investigate how the experience of participating in a TCM might impact our sense of self and our relationships with others. By addressing these areas, we can move towards a more comprehensive understanding of the complex ethical landscape of technologically enabled collective minds and prepare ourselves for the challenges that lie ahead.

Abstract

A growing number of technologies are currently being developed to improve and distribute thinking and decision-making. Rapid progress in brain-to-brain interfacing and swarming technologies promises to transform how we think about collective and collaborative cognitive tasks across domains, ranging from research to entertainment, and from therapeutics to military applications. As these tools continue to improve, we are prompted to monitor how they may affect our society on a broader level, but also how they may reshape our fundamental understanding of agency, responsibility, and other key concepts of our moral landscape.

In this paper we take a closer look at this class of technologies – Technologies for Collective Minds – to see not only how their implementation may react with commonly held moral values, but also how they challenge our underlying concepts of what constitutes collective or individual agency. We argue that prominent contemporary frameworks for understanding collective agency and responsibility are insufficient in terms of accurately describing the relationships enabled by Technologies for Collective Minds, and that they therefore risk obstructing ethical analysis of the implementation of these technologies in society. We propose a more multidimensional approach to better understand this set of technologies, and to facilitate future research on the ethics of Technologies for Collective Minds.

Merging Minds: The Conceptual and Ethical Impacts of Emerging Technologies for Collective Minds
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(Featured) Philosophical foundation of the right to mental integrity in the age of neurotechnologies

Philosophical foundation of the right to mental integrity in the age of neurotechnologies

Andrea Lavazza and Rodolfo Giorgi argue that the development and use of neurotechnology present new challenges to privacy, mental integrity, and autonomy, necessitating a reevaluation of existing ethical frameworks and the introduction of new rights to protect individuals against potential threats to these fundamental aspects of human dignity.

The authors first examine the concept of intentionality, highlighting its importance for understanding the subjective and first-person perspective of mental experiences. They argue that neurotechnology poses a risk to intentionality by potentially manipulating or monitoring individuals’ mental processes. This risk extends to the first-person perspective, as the development of brain-computer interfaces could blur the boundaries between the self and external entities, undermining the sense of ownership and agency that is integral to personal identity.

The paper further discusses the significance of autonomy in moral decision-making and identity-building. Drawing upon moral constructivism, the authors contend that privacy and mental integrity are crucial for individuals to engage in the process of moral self-determination. They assert that neurotechnology has the potential to interfere with this process, leading to misinterpretations of mental states and behaviors, and ultimately hindering individuals’ ability to make autonomous choices and form their own moral judgments.

This research contributes to broader philosophical issues by shedding light on the complex relationship between emerging neurotechnology and fundamental aspects of human nature, such as intentionality, autonomy, and personal identity. It underscores the importance of establishing a right to mental integrity in order to protect these essential elements of human dignity in a world increasingly influenced by advancements in neuroscience and technology.

For future research, it is vital to investigate the ethical and legal implications of the right to mental integrity, delineating its scope and limitations in relation to neurotechnology. This may include examining the potential consequences of different types of interventions, ranging from non-invasive monitoring to direct manipulation of brain states. Additionally, interdisciplinary collaboration between philosophers, neuroscientists, and policymakers will be crucial to developing comprehensive ethical guidelines that address the profound challenges posed by the ongoing development and implementation of neurotechnology in various domains of human life. By bridging these disciplines, we can ensure that the protection of mental integrity remains a central consideration as we navigate the uncharted territory of human-machine interaction.

Abstract

Neurotechnologies broadly understood are tools that have the capability to read, record and modify our mental activity by acting on its brain correlates. The emergence of increasingly powerful and sophisticated techniques has given rise to the proposal to introduce new rights specifically directed to protect mental privacy, freedom of thought, and mental integrity. These rights, also proposed as basic human rights, are conceived in direct relation to tools that threaten mental privacy, freedom of thought, mental integrity, and personal identity. In this paper, our goal is to give a philosophical foundation to a specific right that we will call right to mental integrity. It encapsulates both the classical concepts of privacy and non-interference in our mind/brain. Such a philosophical foundation refers to certain features of the mind that hitherto could not be reached directly from the outside: intentionality, first-person perspective, personal autonomy in moral choices and in the construction of one’s narrative, and relational identity. A variety of neurotechnologies or other tools, including artificial intelligence, alone or in combination can, by their very availability, threaten our mental integrity. Therefore, it is necessary to posit a specific right and provide it with a theoretical foundation and justification. It will be up to a subsequent treatment to define the moral and legal boundaries of such a right and its application.

Philosophical foundation of the right to mental integrity in the age of neurotechnologies
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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