Şerife Tekin

With a proliferation of new technologies and advances in curriculum development and pedagogy, teachers need guidance on integrating these advances into their practices. What is particularly important for teachers is the need to balance new advances in the field with a vision for teaching children with a wide variety of experiences, identities, languages, and needs, necessitating a culturally responsive approach to teaching the whole child as a human being. Rapid advances in generative Artificial Intelligence (AI) accelerated the need for clear guidance about integrating this technology through a culturally responsive approach to teaching and learning. This paper critically examines the existing guidance for teachers regarding using AI to plan and enact culturally responsive lesson plans. Written by a multidisciplinary team of authors, the paper will evaluate the advice offered by professional organisations for the various disciplines (e.g., mathematics, science, literacy, etc.), highlight their strengths and shortcomings, and make recommendations as to how they can be improved to meet the needs of today’s classrooms.

Reclaiming The Self in Psychiatry: Centering Personal Narratives for a Humanist Science argues that contemporary scientific psychiatry has sidelined its most essential object of study: the self. Drawing on first-person testimonies of individuals experiencing mental distress, alongside resources from cognitive and behavioral sciences, the book shows that disruptions to agency, narrative coherence, embodiment, relationality, and self-concept are not ancillary features of mental disorder but central manifestations of it. Psychiatry’s methodological commitment to reliability and objectivity—shaped historically by the desire to establish itself as a scientific field—has produced an evidentially thin and ethically fraught framework that obscures these self-related phenomena. To address this gap, the book develops the Multitudinous Self Model (MuSe), a scientifically tractable, empirically grounded framework that organizes selfhood across physical, experiential, social, conceptual, and narrative dimensions. MuSe redefines objectivity as a participatory and integrative practice that brings scientific, clinical, and first-person forms of knowledge into constructive dialogue. By centering the self as a modeling target, Reclaiming the Self in Psychiatry offers a vision of psychiatric science that is more empirically adequate, ethically responsive, and capable of supporting human flourishing.

Psychiatry is a branch of medicine that aims to scientifically understand the causes of mental disorders and develop effective clinical interventions to address the needs of those experiencing them. Philosophy of psychiatry is concerned with conceptual and practical issues pertaining to mental disorders, their diagnosis, scientific investigation, ethical treatment, and experiences of individuals affected by them, as well as philosophical issues on the place of psychiatry in broader areas of inquiry in medicine and bioethics. It encompasses a variety of topics of interest not only to philosophers, but also to the general public, including the definition of mental disorders, their diagnosis according to medical manuals, features of particular disorders (e.g., substance abuse disorders, depression, autism spectrum disorders), the sources of information about mental disorders (e.g., fMRI studies, clinical trials, first-person reports), ethical issues surrounding the clinical treatment of individuals with mental disorders, and cognate concepts such as the self, rationality, and responsibility. A thoroughly interdisciplinary form of inquiry, philosophy of psychiatry developed out of both the traditional areas of philosophy, including metaphysics, epistemology, philosophy of science/cognitive science, social and political philosophy, philosophy of medicine, and ethics, and clinical contexts pertaining to medical treatment of mental disorders or mental illnesses. In the last few decades, however, philosophy of psychiatry has evolved into a cutting-edge and popular subdiscipline of philosophy in its own right, with its own dedicated journals, books, book series, societies, and conferences. This entry provides an overview of important recent debates in philosophy of psychiatry as they map onto the fundamental areas of philosophy. §1 introduces the terminology; §2 focuses on metaphysics, epistemology, philosophy of science, and medicine related themes; §3 examines themes in philosophy of mind, cognitive science, and neuroscience; §4 turns to social, political, and feminist philosophy; §5 covers issues in ethics and applied ethics.

Schizophrenia is a mental disorder with high rates of prevalence in the United States and elsewhere (Jablensky 1999). It is listed as the ninth leading cause of disability worldwide (Thaker and Carpenter 2001). Yet, precisely what schizophrenia is, how its etiology unfolds, and which treatments are most effective in treating it remain largely controversial. It is diagnosed primarily according to the criteria provided by the Diagnostic and Statistical Manual of Mental Disorders (DSM). However, these particular criteria have been the target of a number of criticisms, resulting in a Kuhnian crisis in the DSM-led schizophrenia research. There are a number of anomalous research findings inconsistent with the dominant DSM paradigm. Consider a few. First, DSM’s phenomenology-oriented critics suggest that the symptoms and signs listed in the DSM’s schizophrenia category do not match the clinical realities of the patients (e.g., Parnas 2000; Parnas and Handest 2003). There are also environmental, cultural, and gender-related variations in the expression of the symptoms (American Psychiatric Association 2013, 103). Second, there are concerns about the DSM’s reliability; there is no uniformity between the DSM and the International Classification of Diseases (ICD) with respect to schizophrenia diagnostic criteria (Jansson et al. 2002). In fact, based on such findings, some philosophers and psychologists including Richard P. Bentall (1993; see also this volume), Mary Boyle (1990), and Jeffrey Poland (2007) have argued against considering schizophrenia as a discrete illness. For instance, Bentall challenges the view that what are considered the symptoms of this condition fall neatly under the label “schizophrenia,” and that hallucinations and delusions associated with the condition are meaningless. In this regard, the concept of “schizophrenia” is similar to a number of other concepts; for example, “phlogiston” and “the luminiferous ether” were widely employed by scientists for a time but turned out to be scientifically misleading (Bentall 1993, 227). These concerns are mirrored also in the psychiatric community, especially in the United Kingdom and continental Europe, where there is an international debate on what to replace the concept of schizophrenia with. Third, there are cultural variations in recovery rates; for example, third-world countries have higher recovery rates than first-world countries (Warner 2009). Finally, there is skepticism raised by the National Institute of Mental Health (NIMH) about the DSM-led research program’s efficacy in encouraging research into the genetic and neural etiology of mental disorders; these concerns are applicable to schizophrenia (Insel 2013). It remains to be seen how schizophrenia will be targeted by the NIMH’s Research Domain Criteria project—the proposed research alternative to the DSM-5. Psychiatric research on schizophrenia is currently undergoing a period of extraordinary science, to apply Kuhn’s terminology to the current situation in psychiatry, with many alternative research programs investigating the illness using different assumptions and methodologies. As the struggles the DSM-led research faces are now “more generally recognized as such by the profession,” trust in the dominant DSM-led research paradigm is shaken, and “numerous partial solutions to the problem” are made available (Kuhn 1962, 82–83). I use philosophical tools in this chapter to evaluate one of these alternative research approaches that I call “phenomenology– neuroscience partnership” (PNP). First, in the “Phenomenological Approach to Schizophrenia” section, I lay out the phenomenological approach to schizophrenia that is critical of the DSM-led research. Next, in the “Phenomenology–Neuroscience Partnership in Schizophrenia Research” section, I focus on the PNP that takes this phenomenological approach as a starting point to investigate schizophrenia, and I address its shortcomings. Then, in the “Adjudication of Phenomenology–Neuroscience Partnership Paradigm” section, I conclude by pointing out the strengths of the PNP and offer prescriptions for its improvement.

A climate of crisis and controversy exists in contemporary mental health research and practice, stemming partially from tensions within psychiatry. On the one hand, as a branch of medicine, psychiatry aims at clinically addressing the complaints of individuals with mental disorders, including unwanted behavior and the subjective, mental, and first-person aspects of psychopathology (such as feelings of unwarranted guilt and hallucinations); yet there are serious concerns regarding overdiagnosis and overtreatment, andnd there is no consensus on which treatment methods are most effective in addressing mental health problems. On the other hand, as a branch of science, psychiatry targets the objective, embodied, and thirdperson causes and correlates of behavioral problems and mental distress (such as atypical brain mechanisms and genetic anomalies); however, there is no agreement on what kinds of scientific constructs will best help probe these phenomena, and there has been a notable lack of scientific progress. At the center of these controversies is The Diagnostic and Statistical Manual of Mental Disorders (DSM), the psychiatric taxonomy used in the United States and widely around the world, which has been developed to identify both the scientific and clinical targets of psychiatry, as well as to be used in the service of sociological, pedagogical, and forensic projects (American Psychiatric Association 2013, xli; American Psychiatric Association 1994, xv). It is widely agreed, however, that the DSM is seriously flawed. Common and widely acknowledged problems include problems concerning the validity and reliability of the psychiatric diagnoses, poorly understood comorbidities, heterogeneity of diagnostic groupings, and overinclusiveness of diagnostic criteria. As a consequence, the DSM is seen by many as a major source of the current crisis in mental health research and practice. The goal of the present volume is to focus directly on research-related questions in this crisis and to explore both the nature and sources of the crisis as well as whether and, if so, how, it can be responded to, or at best, overcome. There has been an increased focus on the scientific research of mental disorders, not only by scientists, but also by philosophers. However, both in areas where this research exemplifies the problems characteristic of the crisis (e.g., problems of scientific validity of DSM categories) and in areas where it constitutes a source of responses to those problems (e.g., the conviction that the more we understand the brain, the better we will address mental disorders), a thorough philosophical investigation of the current crisis is missing. Moreover, given the existing crisis, whether and, if so how, research in psychiatry can make progress is not adequately addressed in the relevant literature.

Contemporary scientific and philosophical debates on addiction center on two models. In the brain disease model, drug addiction is a “chronic and relapsing brain disease that results from the prolonged effects of drugs on the brain” (Leshner 1997: 45). In contrast, the self or person model individuates addiction in folk psychological terms, as a kind of behavior marked by he repeated use of a drug of choice (DoC) and the difficulty involved in quitting (Satel and Lilienfeld 2013; Flanagan 2013a; Tekin et al. 2017). On the one hand, the brain disease model promotes cellular-level understanding, painting the addict as a biological organism. It takes seriously recent advances in brain imaging methods and the increased sophistication in the neuroscientific modeling of the brain’s reward system—the mesolimbic dopamine system—and promotes pharmaceuticals to target and rebalance the chemical anomalies in it. On the other hand, the self model sees the addict as a person or intentional system (Dennett 1971) who acts according to beliefs and desires and responds to complex historical, environmental, and interpersonal aspects of life. It points to the personal-socio-cultural markers of addiction and promotes social, psychological, and behavioral intervention strategies, e.g., encouraging the addict to change the interpersonal environment that triggers and perpetuates addictive behavior, to receive intensive psychotherapy, to cultivate meditation, and so on. Although each model accepts the validity of the other, to a certain point, there is a prima facie tension between them. The brain disease model acknowledges the environmental, social, and cultural factors in the development of addiction, but its proponents often favor neuroscientific work in lieu of other research paradigms, e.g., social sciences and cognitive sciences. The self model acknowledges the importance of the cellular-level understanding of addiction, but its adherents warn of the dangers of neuro-enthusiasm (Satel and Lilienfeld 2013), i.e., the promotion of scientific and clinical approaches to target the person. In this chapter, I propose using Ken Schaffner’s framework for thinking about reductionism in science (Schaffner 2013) to resolve the issue. In this framework, the brain disease model moves in the direction of what Schaffner calls “sweeping reductionism,” with the self model moving towards “creeping reductionism.” Because of the virtues of creeping reductionism, I argue the self model has more promise for addiction research. I start the chapter with a brief overview of Schaffner’s framework for reduction in science, describing his notions of “sweeping” and “creeping” reductionism. I apply this framework to the brain disease and the self models, noting the epistemic advantages of committing to the latter. In my analysis, I draw on scientific research and clinical work on addiction; to this I add first-person accounts of those influenced by addiction, a seldom used but rich resource(Tekin 2016).

A primary goal of psychiatric epistemology is to identify the properties of mental disorders that are relevant for developing effective interventions. Sources of information individuating these properties include scientific research on mental disorders (e.g., clinical drug trials), data emerging from clinical settings (e.g., case studies), and first-person reports of those suffering from mental disorders or those who witness or observe such suffering. While dominant scientific frameworks in psychiatry recognize and rely on scientific and clinical data in their efforts to advance the science of mental disorders, they are not similarly committed to directly using the first-person perspectives of patients. More specifically, individuals who suffer from and are diagnosed with mental disorders have not systematically been made part of the scientific inquiry into mental disorders. This chapter calls for this problem to be rectified in scientific psychiatry. I argue that individuals diagnosed with mental disorders are “experience-based” experts on mental disorders and, as such, must be included in the scientific research aiming to expand knowledge in psychiatry. In developing this argument, I challenge the presupposition that individuals with mental disorders lack the expertise to make meaningful contributions to psychiatric epistemology. In the first section of the chapter I review the history of the creation and revision processes of the Diagnostic and Statistical Manual of Mental Disorders (DSM) to illustrate how patients have historically been left out of scientific inquiry in mental disorders, tracing it to a presupposition that they lack the expertise required for scientific investigation. In the second section critically examine the definition of expertise deployed by the American Psychiatric Association (APA), creator of the DSM, and develop an alternative definition by identifying patients as experience-based experts. My discussion is guided by Owen Flanagan’s natural method and the account of expertise developed by third-wave science studies scholars. My argument for including patients in psychiatric research is primarily conceptual: I challenge the assumption that individuals with mental disorders lack expertise in the scientific investigation of mental disorders. Unfortunately, while a discussion of how to develop better scientific policies (or scientific paradigms) involving patients would be interesting, it is beyond the scope of this chapter.

There is controversy concerning the relationship between stigma and the conceptualization of mental illness as a biomedical disease. Proponents of the biomedical model argue that conceptualizing mental illness as a biochemical disease benefits patients, because it not only enables them to receive medical treatments but also helps them avoid the stigmas associated with mental illness. Opposing this position, others suggest that biomedical causal trajectory further contributes to stigma. When considering individuals' sense of well‐being while living with a mental illness, we believe that this debate detracts us from the most important aspects of removing stigma and enabling patients to develop fulfilling lives. Thus, using patient memoirs, we refocus attention on the patient experience itself, searching for how patients' memoirs can illustrate roads to recovery, resilience, and coping. We examine first what facilitates patients to live a fulfilling life and next, what obstacles they identify to a fulfilling life in the face of their encounter with mental illness.

Recent methodological debates on the value of traditional conceptual analysis in philosophy (Strevens 2019; Machery 2017) have implications for both philosophy of psychiatry and scientific psychiatry. For example, the idea that concepts are universal in nature has been criticized, with experimental philosophers showing how different social groups have differing intuitions about the application of certain concepts. This has led to the development of new approaches in philosophical inquiry, such as naturalized conceptual analysis and conceptual engineering (Machery 2017). Taking cues from this debate, this chapter calls for a naturalized conceptual analysis of the concept of ‘expertise’ in psychiatry. I propose using the methods of both empirically informed research and experimental philosophy to clarify and perhaps modify the defi nition of ‘expertise’ operative in psychiatry. A definition of expertise modifi ed in this fashion could include not only those who are formally trained in psychopathology and hold academic degrees, e.g., MD, PhD, but also those with direct experience of mental disorders. In fact, interdiciplinary fields as critical disability studies and mad studies have already articulated such ideas about expertise, generating important body of work authored by experts by experience, yet traditional scientific psychiatry has not directly engaged with this literature nor critically examined their own assumptions about expertise (see, for example, Carlson 2010). A modified notion of expertise, I believe, through naturalized conceptual analysis and experimental philosophy would help psychiatry better align with its fundamental goals as a branch of medicine, enhancing the understanding of mental disorders and facilitating eff ective and ethical treatments and care. I start with an overview of the main tenets of naturalized conceptual analysis and illustrate how its insights might be helpful for scientifi c psychiatry and philosophy of psychiatry. I then turn to the existing notions of ‘expertise’ in scientifi c psychiatry and philosophy of psychiatry, with a focus on the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the Research Domain Criteria (RDoC), conceptions of expertise in some patient/consumer/survivor/ex-patient groups, and the understanding of some clinicians and philosophers. Th is examination will illustrate that first, there is no consistent and unified notion of expertise in psychiatry, and second, naturalized conceptual analysis could benefi t psychiatric epistemology. Using insights from the DSM, RDoC, and patient movement frameworks, I conclude the chapter with a sketch of what experimental studies about the concept of expertise might demonstrate. My hope is to suggest to philosophers of psychiatry and clinicians (if not to convince them) that experimental philosophy has the potential to expand our knowledge of what it means to be an expert on mental disorders.

The last few decades have witnessed a substantial increase in the number and needs of individuals with mental disorders and other mental health concerns. Unfortunately, the increase has not been met with a parallel rise in treatments and interventions available to these communities. Consider the 2016 statistics in the US: 18.3% of all US adults were diagnosed with a mental disorder, and of these, only 43.1% received some kind of treatment, e.g., inpatient or outpatient counseling or prescription medication (Substance Abuse and Mental Health Services Administration 2017). The need-availability gap widened during the COVID-19 pandemic (KFF analysis of US Census Bureau, Household Pulse Survey, 2020–2021). For example, about four in ten adults in the US reported symptoms of anxiety or depressive disorder, up from one in ten reporting these symptoms from January to June 2019. Reasons for the gap between the rising demand for mental health treatment and available resources include the stresses of the pandemic, shortage of mental health professionals, especially in remote and rural areas, individuals’ inability to recognize their mental health problems, high cost of mental healthcare, stigma attached to having mental disorders, and increased needs of vulnerable populations, such as refugees, immigrants, or veterans. In the face of these problems, recent advances in applications of artificial intelligence in medicine and psychiatry give some reason for optimism, as their proponents believe these technologies will fill the need-availability gap by enhancing resources for mental healthcare. One technology that has generated significant enthusiasm is the artificially intelligent chatbots, or what I will call Chat_Bots. Chat_Bots are purported to provide Cognitive Behavior Therapy (CBT) to their users with the aim of helping them improve their mental health and/or address their mental disorders. In the face of the increased interest in these technologies among researchers and the public at large, a plethora of connected questions has emerged: Are Chat_Bots effective tools for mental disorder diagnosis and treatment? Which ethical standards should guide their development and use? Must research on the artificial intelligence-assisted behavioral intervention technology be prioritized over improving other diagnostic and treatment strategies, such as in-person psychotherapy? Should this technology be funded publicly or privately? This chapter will examine some of the ethical issues concerning the use of Chat_Bots. First, I will review what Chat_Bots are and the contexts in which they are used, with some examples. Second, I will critically evaluate the promises of the proponents of Chat_Bots technology and address the ethical concerns lying therein.

There are periods of time in humanity when a true inflection point in advancement occurs. Consider the industrial revolution, the (first) space race, or the dawn of the information age. It is not an exaggeration to argue that we are entering another such period, that of the rapid exploration of both nearby and outer space. NASA is planning a return to the Moon (planned ~2027) and then Mars and recently launched the ~$10B James Webb Space Telescope (December 25, 2021). New telescopes or probes could find evidence of life on distant exoplanets (planets around other stars) or solar system bodies (i.e., Mars, Europa, etc.) in the coming decades. SpaceX has already revolutionized the launch industry, with the global industry expected to be a $1.1 trillion market by 2040 (or much sooner). Mining of asteroids, harvesting Moon resources, and militarization of space is planned. These developments raise a host of questions, such as, what kind of scientific activity is space exploration? What are the ethical implications of these activities? How can the problems of past “frontier” exploitations be avoided? How can we protect even microbial life from terrestrial contamination (or annihilation), or should we care? What is the definition of life (there is no generally accepted definition), and what rights does it have? What are the ethics of billionaire joyrides to space given their climate footprint? Such questions have been debated by scientists, philosophers, and novelists for thousands of years, but only now do we have the formidable science and technology to address them. In this paper we describe the creation and delivery of an Honors College course called the “The Philosophy of Space Exploration (PoSE),” jointly taught by professors of philosophy and astrophysics. The fusion of these disciplines afforded a powerful exploration of the questions raised above. Further, we taught this course entirely online, where discussion sessions were led entirely in a virtual reality environment, the first of its kind at the University of Texas at San Antonio.  

The question “What is the relationship between the self and mental disorder?” is especially important for mental health professionals interested in understanding and treating patients, as most mental disorders are intimately tied to self‐related concerns, such as loss of self‐esteem and self‐control, or diminished agency and autonomy. Philosophy, along with the cognitive and behavioral sciences, offers a wealth of conceptual and empirical resources to answer this question, as the concepts of the self and psychopathology have occupied a central place in these fields since their inception. Interestingly, and unfortunately, however, scientific psychiatry, in its approach to mental disorder as primarily a cluster of signs and symptoms has been slow in acknowledging the advances in conceptualizing and investigating mental disorders in relation to the self. The article addresses this problem and offers solutions for better cross‐fertilization between empirical and philosophical inquiry into the self and psychiatric research on mental disorders. The first part evaluates what empirically informed philosophical inquiry and philosophically informed empirical inquiry offer to the examination of the relationship between the self and mental disorders. The second part argues that scientific psychiatry has missed opportunities to find effective treatments for mental disorders because of its insularity from debates in naturalistic philosophy and cognitive and behavioral sciences. Psychiatry has much to gain from the conceptual, scientific, and clinical resourcefulness of philosophy's collaboration with the cognitive and behavioral sciences.

Reclaiming the Self in Psychiatry: Centering Personal Narratives for Humanist Science diagnoses the fundamental problem in contemporary scientific psychiatry to be a lack of a sophisticated and nuanced engagement with the self and proposes a solution—the Multitudinous Self Model (MuSe). MuSe fulfils psychiatry’s twin commitments to patients’ flourishing and scientific objectivity. Marshalling the conceptual and empirical resources from testimonies from individuals diagnosed with mental disorders, substantive research in cognitive science, and empirically informed philosophy, MuSe provides clinicians, scientists, and patients pathways to respond to mental distresses and disorders. This framework boosts psychiatry’s relationship to science by facilitating expansive notions of expertise and objectivity in which some patients are recognized as “experience-based experts” whose contributions to psychiatric knowledge are indispensable. Şerife Tekin draws the contours of a future for psychiatry that is grounded in philosophy, medical humanities, and social sciences as much as physiology and neuroscience. This book is an ideal read for professional psychiatrists and philosophers of psychiatry who are interested in the philosophy of mental health.

In April 2024, the United States Food and Drug Administration approved the first digital application to treat major depression in adults 22 and older.1 The app—Rejoyn—joins a growing list of artificial intelligence (AI)-based platforms designed to treat mental illness.2 These tools range from chatbots to gamified cognitive behavioral therapy (CBT), to machines that emulate human therapists. Given the significant barriers to accessing mental health care, these technologies have been pitched as a means of addressing the current mental health crisis among youth.3 Although there is a growing literature exploring the ethical implications of the use of AI in mental health care, their use in pediatric populations remains underexplored. This commentary explores the use of large language model (LLM)-based digital applications in pediatric mental health care, with the goal of beginning to map this terrain. LLMs are a form of AI designed to generate human-like responses to natural language prompts. These models use deep learning techniques, specifically transformer architectures, to analyze patterns in language and create coherent, contextually relevant responses. We will refer to LLM-based “conversational agents” colloquially as “therapy bots” or “AI chatbots” interchangeably throughout. These are systems that use LLMs, or other advanced natural language processing systems, to simulate the utterances of a human therapist as part of a conversation with a pediatric patient.

There is increased enthusiasm about the use of Artificial Intelligence (AI) technologies in psychotherapy. Notably, AI psychotherapy chatbots are increasing in popularity, especially since the US Food and Drug Administration (FDA) gave one of these apps breakthrough device designation. This article raises concerns about the lack of consideration of potential harms of this technology for clinical trial participants, and current and future users. We outline what these harms might be, by turning to the Belmont Report and the existing literature on harms of (typical) psychotherapy and conclude with two recommendations. Note that our goal is not to articulate doomsday fears regarding the use of AI in psychotherapy contexts; rather we offer a constructive proposal in thinking about the potential harms of these tools and invite clinicians, patients, developers, researchers, policymakers and funding agencies to work together to augment the benefits of these tools and minimize their potential harms.

In a compelling and provocative paper, ‘Solving the Self-Illness Ambiguity: The Case for Construction Over Discovery,’ Sofia M.I. Jeppsson distinguishes two ways of addressing the self-illness ambiguty problem. The first is the Realist Solution, which postulates a pre-existing border between the self and the illness and frames the goal of treatment in psychiatry as helping the patient ‘discover’ this boundary. Addressing the shortcomings of the Realist Solution, both in terms of its feasibility and possible outcomes, Jeppsson proposes and defends the Constructivist Solution, according to which the patient, through self-reflection and deliberation with others, including the clinicians, decides, which parts of her experiences they identify with themselves and which parts they attribute to their illness. This paper critically evaluates Jeppson’s arguments and addresses some of the shortcomings of Jeppsson’s positive argument, i.e. the Constructivist Solution.

This paper challenges the exclusion of patients from epistemic practices in psychiatry by examining the creation and revision processes of the Diagnostic and Statistical Manual of Mental Disorders (DSM), a document produced by the American Psychiatric Association that identifies the properties of mental disorders and thereby guides research, diagnosis, treatment, and various administrative tasks. It argues there are epistemic—rather than exclusively social/political—reasons for including patients in the DSM revision process. Individuals with mental disorders are indispensable resources to enhance psychiatric epistemology, especially in the context of the crisis, controversy, and uncertainty surrounding mental health research and treatment.

Psychotherapy is a well-established, efficacious, and fully accepted treatment for mental disorders and psychological problems. Psychotherapy is an interpersonal practice engaging patient values, interests, and personal meanings at every step. Thereby, psychotherapy abounds with moral issues. In psychotherapy ethics, numerous moral issues converge, including self-determination or autonomy, decision-making capacity and freedom of choice, coercion and constraint, medical paternalism, boundaries between health and illness, insight into illness and the need for therapy, dignity, under- and overtreatment, and much more. The Oxford Handbook of Psychotherapy Ethics covers the whole range of ethical issues in the heterogenous field of psychotherapy, closing a commonly perceived gap between ethical sensitivity, technical language, and knowledge among psychotherapists. The primary audience is psychotherapists in clinical practice. Furthermore, the Oxford Handbook of Psychotherapy Ethics addresses the whole range of professionals providing mental health services beyond psychology and medicine including counselors, social workers, nurses, or ministers. The Oxford Handbook of Psychotherapy Ethics is not only intended for a clinical audience but also for a philosophical/ethical audience. The goal of the is to explicitly link the two disciplines by fostering a productive dialogue between them, enriching both the psychotherapeutic encounter and the ethical analysis and sensitivity within and outside the clinic.

The question “What is the relationship between the self and mental disorder?” is especially important for mental health professionals interested in understanding and treating patients, as most mental disorders are intimately tied to self‐related concerns, such as loss of self‐esteem and self‐control, or diminished agency and autonomy. Philosophy, along with the cognitive and behavioral sciences, offers a wealth of conceptual and empirical resources to answer this question, as the concepts of the self and psychopathology have occupied a central place in these fields since their inception. Interestingly, and unfortunately, however, scientific psychiatry, in its approach to mental disorder as primarily a cluster of signs and symptoms has been slow in acknowledging the advances in conceptualizing and investigating mental disorders in relation to the self. The article addresses this problem and offers solutions for better cross‐fertilization between empirical and philosophical inquiry into the self and psychiatric research on mental disorders. The first part evaluates what empirically informed philosophical inquiry and philosophically informed empirical inquiry offer to the examination of the relationship between the self and mental disorders. The second part argues that scientific psychiatry has missed opportunities to find effective treatments for mental disorders because of its insularity from debates in naturalistic philosophy and cognitive and behavioral sciences. Psychiatry has much to gain from the conceptual, scientific, and clinical resourcefulness of philosophy's collaboration with the cognitive and behavioral sciences.

Advances in applications of artificial intelligence and the use of data analytics technology in biomedicine are creating optimism, as many believe these technologies will fill the need-availability gap by increasing resources for mental health care. One resource considered especially promising is smartphone psychotherapy chatbots, i.e., artificially intelligent bots that offer cognitive behavior therapy to their users with the aim of helping them improve their mental health. While a number of studies have highlighted the positive outcomes of using smartphone psychotherapy chatbots to handle various anxiety related problems no conclusive data illustrate their effectiveness or warrant their use in mental illness diagnosis and treatment settings. Yet smartphone psychotherapy is highly endorsed by experts in the field of mental health research. In this paper, I focus on the specific features of smartphone psychotherapy chatbots intended for the diagnosis and treatment of mental illness and criticize three popular promises; i.e., they enable early diagnosis and intervention through digital phenotyping; they defy the stigma of mental illness diagnosis and treatment; they offer increased access to mental health treatment globally. Going against the popular enthusiasm, I argue smartphone psychotherapy chatbots have epistemic and ethical limitations in the diagnosis and treatment of illnesses. In light of these, I encourage researchers, clinicians, policy makers, patients, and caregivers to pause before jumping on the artificial intelligence bandwagon to seek solutions for mental illness on the grounds of these three promises.