Is Artificial Intelligence Bringing Bias into Mental Health Treatment?

Is Artificial Intelligence Bringing Bias into Mental Health Treatment?

Curt and Katie chat about the responsibility therapists hold when they use AI applications for their therapy practices. We explore where bias can show up and how AI compares to therapists in acting on biased information. This is a continuing education podcourse.

Transcripts for this episode will be available at mtsgpodcast.com!

In this podcast episode we talk about whether therapists or AI are more biased

With the inclusion of artificial intelligence tools into psychotherapy, there is more access to mental health treatment by a larger portion of the world. This course addresses the question “Do the same biases that exist in in-person delivered psychotherapy exist in AI delivered treatment?” at the awareness, support, and intervention levels of mental health treatment.

How is machine learning used in “AI” for therapists?

·       There are different types of AI used in mental health, machine learning, neural networks, and natural language processing

·       AI can be used for awareness, support, and/or intervention

·       There is a potential for bias within AI models

Where can bias come in when AI models are used in mental health?

·       Source material, like the DSM

·       Human error in the creation

·       Cultural humility and appropriateness

Are human therapists less biased than AI models in diagnosis and mental health intervention?

·       The short answer is no

·       A study shows that ChatGPT is significantly more accurate than physicians in diagnosing depression (95% or greater compared to 42%)

·       ChatGPT is less likely to provide biased recommendations for treatment (i.e., they will recommend therapy to people of all socioeconomic statuses)

·       There is still possibility for bias, so diverse datasets and open source models can be used to improve this

What is a potential future for mental health treatment that includes AI?

·       Curt described therapy practices being like Pilots and autonomous planes, with the ability to provide oversight, but much less intervention

·       Katie expressed concern about the lack of preparation that therapists have for these dramatic shifts in what our job looks like

Key takeaways from this podcast episode (as curated by Otter.ai)

·       Enhance the training and validation of AI algorithms with diverse datasets that consider intersectionality factors

·       Explore the integration of open-source AI systems to allow for more robust identification and addressing of biases and vulnerabilities

·       Develop educational standards and processes to prepare new therapists for the evolving role of AI in mental healthcare

·       Engage in advocacy and oversight efforts to ensure therapists have a voice in the development and implementation of AI-powered mental health tools

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