Ethical Considerations While Using Large Language Models-View Presentation

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Darlene King, an assistant professor at UT Southwestern, explores the ethical considerations of using AI in mental health care. She outlines AI's historical development, including periods of rapid advancements and slow progress. AI's accessibility has increased significantly with tools like ChatGPT, facilitating broader use. In clinical settings, the risk levels associated with AI vary, from administrative tasks to direct clinical decisions. King emphasizes that AI systems can inherit bias from data, affecting their output and accuracy. Current liability in using AI lies with clinicians, raising questions about shared accountability.<br /><br />King discusses AI's potential for harm, such as data leaks, misinformation, and patient safety concerns. She highlights examples of AI misuse, like chatbots influencing sensitive behaviors, and ethical issues like copyright infringement by generative AI models. Additionally, studies have revealed AI's automation bias, leading to unwarranted trust.<br /><br />King advises caution when integrating AI into healthcare, urging clinicians to remain informed about potential biases and hallucinations. She stresses the importance of HIPAA compliance and securing patient data. The session ends with recommendations for assessing AI's clinical utility, emphasizing the need for mental health professionals to actively engage in the technology's development and deployment.

Keywords

AI in mental health

ethical considerations

AI bias

clinical settings

patient safety

misuse of AI

automation bias

HIPAA compliance

AI accountability