KI-gestützte Selbstvermessung der Psyche: Philosophisch-ethische Implikationen

Zusammenfassung

Ziel Zunehmend werden KI-basierte Anwendungen entwickelt, die Nutzende dabei unterstützen sollen, ihre Emotionen, Überzeugungen und Verhaltensmuster digital zu erfassen, zu verwalten und zu verändern. Solche Formen der Selbstvermessung im Bereich der menschlichen Psyche können vielfältige medizinische Vorteile in Diagnostik, Prävention und Therapie haben. Dieser Beitrag geht der Frage nach, welche philosophisch-ethischen Herausforderungen gegenüber diesen Vorteilen abgewogen werden sollten.

Methode Zunächst werden einige KI-basierte Anwendungen zur Selbstvermessung psychischer Eigenschaften und Prozesse skizziert. Im Anschluss werden relevante philosophisch-ethische Implikationen aufgezeigt.

Ergebnisse Folgende Aspekte erweisen sich als normativ relevant: Verbesserung versus Verminderung von Selbstbestimmungsfähigkeit; Verbesserung der Selbstkenntnis versus Entfremdung; positive versus negative Aspekte eigenverantwortlicher Gesundheitsfürsorge; epistemische Herausforderungen von KI-Anwendungen; Schwierigkeiten von konzeptionellen und normativen Festlegungen in den Anwendungen.

Abstract

Objective AI-based applications are increasingly developed to support users to digitally record, manage and change their emotions, beliefs and behavior patterns. Such forms of self-tracking in the mental sphere are accompanied by a variety of medical benefits in diagnostics, prevention, and therapy. This article pursues the question of which philosophical-ethical implications must be taken into account when dealing with these advantages.

Methods First, some AI-based applications for self-tracking of mental characteristics and processes are outlined. Subsequently, relevant philosophical-ethical implications are presented.

Results The following aspects prove to be normatively relevant: improvement versus reduction of self-determination; improvement of self-knowledge versus alienation; positive versus negative aspects of self-responsible health care; epistemic challenges of AI applications; difficulties of conceptual and normative definitions in the applications.

Schlüsselwörter

Selbstvermessung - Künstliche Intelligenz - E-Health - psychische Gesundheit - psychische Störung

Keywords

self-tracking - artificial intelligence - e-health - mental health - mental disorder

Publikationsverlauf

Artikel online veröffentlicht:
02. März 2021

• Literatur

• 1 Fakhoury M. Artificial Intelligence in Psychiatry. In: Kim Y-K. ed. Frontiers in Psychiatry. Advances in Experimental Medicine and Biology. Singapore: Springer Singapore; 2019: 119-125
• 2 Sharon T. Self-tracking for health and the quantified self: Re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcare. Philos Technol 2017; 30: 93-121
• 3 Duttweiler S, Gugutzer R, Passoth J-H, Strübing J. Hrsg. Leben nach Zahlen. Self-Tracking als Optimierungsprojekt?. Bd. 10. Digitale Gesellschaft. Bielefeld: transcript; 2016.
• 4 Lupton D. Self-Tracking Modes: Reflexive Self-Monitoring and Data Practices. SSRN Journal 2014;
• 5 Arya mHealth UG. Arya Mood Tracker. Im Internet: Stand: 09.07.2020 http://www.aryaapp.co
• 6 Fegert JM, Baumeister H, Brieger P. et al. Greifswalder Erklärung zur gesellschaftlichen Bedeutung des Bereichs psychische Gesundheit in der Gesundheitsforschung – „Lost in Translation?“. Psychiat Prax 2019; 46: 70-72
• 7 Steel Z, Marnane C, Iranpour C. et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013. Int J Epidemiol 2014; 43: 476-493
• 8 Krystal JH, State MW. Psychiatric Disorders: Diagnosis to Therapy. Cell 2014; 157: 201-214
• 9 Bzdok D, Meyer-Lindenberg A. Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2018; 3: 223-230
• 10 Dwyer DB, Falkai P, Koutsouleris N. Machine Learning Approaches for Clinical Psychology and Psychiatry. Annu Rev Clin Psychol 2018; 14: 91-118
• 11 Elvevåg B, Foltz PW, Weinberger DR. et al. Quantifying incoherence in speech: an automated methodology and novel application to schizophrenia. Schizophr Res 2007; 93: 304-316
• 12 Poulin C, Shiner B, Thompson P. et al. Predicting the risk of suicide by analyzing the text of clinical notes. PLoS ONE 2014; 9: e85733
• 13 Fiske A, Henningsen P, Buyx A. Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy. J Med Internet Res 2019; 21: e13216
• 14 Löbner M, Pabst A, Stein J. et al. Computerized cognitive behavior therapy for patients with mild to moderately severe depression in primary care: a pragmatic cluster 4 randomized controlled trial (@ktiv). Journal of Affective Disorders 2018; 238: 317-326
• 15 Riedel-Heller SG. Computerbasierte Therapien: Eine Ergänzung in der modernen Psychotherapie? – Pro. Psychiat Prax 2017; 44: 10-11
• 16 Joerin A, Rauws M, Ackerman ML. Psychological Artificial Intelligence Service, Tess: Delivering On-demand Support to Patients and Their Caregivers: Technical Report. Cureus 2019; 11: e3972
• 17 Malhi GS, Hamilton A, Morris G. et al. The promise of digital mood tracking technologies: are we heading on the right track?. Evid Based Ment Health 2017; 20: 102-107
• 18 Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol 2008; 4: 1-32
• 19 Balconi M, Grippa E, Vanutelli ME. What hemodynamic (fNIRS), electrophysiological (EEG) and autonomic integrated measures can tell us about emotional processing. Brain Cogn 2015; 95: 67-76
• 20 Dijksterhuis C, de Waard D, Brookhuis KA. et al. Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns. Front Neurosci 2013; 7: 149
• 21 Mohamad Y, Gappa H, Nordbrock G. Experimental Approach on Affective Aware Systems for Disabled Users. Procedia Computer Science 2015; 67: 445-451
• 22 Beauchamp TL, Childress JF. Principles of biomedical ethics. New York: Oxford University Press; 2019
• 23 Sunstein CR. The ethics of nudging. Yale J on Reg 2015; 32: 413
• 24 Goodwin T. Why we should reject ‘nudge’. Politics 2012; 32: 85-92
• 25 Coleman NV, Williams P. Feeling Like My Self: Emotion Profiles and Social Identity. J Consum Res 2013; 40: 203-222
• 26 Yuste R, Goering S, Arcas BAY. et al. Four ethical priorities for neurotechnologies and AI. Nature 2017; 551: 159-163
• 27 Mittelstadt BD, Allo P, Taddeo M. et al. The ethics of algorithms: Mapping the debate. Big Data & Society 2016; 3: 205395171667967
• 28 Floridi L, Cowls J, Beltrametti M. et al. AI4People-An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds & Machines 2018; 28: 689-707
• 29 Goddard K, Roudsari A, Wyatt JC. Automation bias: a systematic review of frequency, effect mediators, and mitigators. J Am Med Inform Assoc 2012; 19: 121-127
• 30 Naslund JA, Aschbrenner KA, Araya R. et al. Digital technology for treating and preventing mental disorders in low-income and middle-income countries: a narrative review of the literature. The Lancet Psychiatry 2017; 4: 486-500