Considerations for the ethical implementation of psychological assessment through social media via machine learning

ABSTRACT

The ubiquity of social media usage has led to exciting new technologies such as machine learning. Machine learning is poised to change many fields of health, including psychology. The wealth of information provided by each social media user in combination with machine-learning technologies may pave the way for automated psychological assessment and diagnosis. Assessment of individuals’ social media profiles using machine-learning technologies for diagnosis and screening confers many benefits (i.e., time and cost efficiency, reduced recall bias, information about an individual’s emotions and functioning spanning months or years, etc.); however, the implementation of these technologies will pose unique challenges to the professional ethics of psychology. Namely, psychologists must understand the impact of these assessment technologies on privacy and confidentiality, informed consent, recordkeeping, bases for assessments, and diversity and justice. This paper offers a brief review of the current applications of machine-learning technologies in psychology and public health, provides an overview of potential implementations in clinical settings, and introduces ethical considerations for professional psychologists. This paper presents considerations which may aid in the extension of the current Ethical Principles of Psychologists and Code of Conduct to address these important technological advancements in the field of clinical psychology.

KEYWORDS:

• Automated assessment
• ethical principles of psychologists
• machine learning in psychology
• social media
• psychology

ACKNOWLEDGMENTS

Special thanks to Drs. Nan Presser and Rebecca Schwartz-Mette for their invaluable support in the preparation of this manuscript.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author.

Additional information

Funding

The author is supported by the National Institutes of Health Grant T32 AA013526.