Algorithmic Bias and Health Equity in Mental Health AI
A 2-hour intermediate CE course for licensed mental health professionals examining how bias enters clinical artificial-intelligence systems, the documented disparities these systems can produce in mental-health care, and the equity frameworks and clinician responsibilities required to use such tools justly. Grounded in peer-reviewed scholarship including Obermeyer et al. (2019), Gianfrancesco et al. (2018), and Char et al. (2018). 12,573 words.
2
CE Hours
What you'll learn
- Describe at least five distinct mechanisms by which bias enters clinical artificial-intelligence systems, and differentiate systematic bias from random noise.
- Analyze documented cases of inequity in healthcare and mental-health algorithms, including the Obermeyer et al. (2019) cost-as-proxy study and electronic-health-record bias.
- Compare competing definitions of algorithmic fairness and explain why several fairness criteria cannot be satisfied simultaneously.
- Apply bias-auditing tools — including model cards, datasheets for datasets, and participatory design — to the appraisal of a mental-health AI tool.
- Implement the clinician responsibilities of informed skepticism, documentation of overrides, advocacy, and cultural humility when integrating AI into clinical decision-making.
Who it's for
Licensed mental health professionals (LPCs, LCSWs, LMFTs, NCCs, psychologists) committed to culturally responsive, equitable use of AI in clinical care.
Approval & credit
CounselorReady is an NBCC-Approved Continuing Education Provider (ACEP #7760). This course awards 2 NBCC-approved CE hours; a certificate is issued on completion. Programs that do not qualify for NBCC credit are clearly identified. CounselorReady is solely responsible for all aspects of the program.