JOURNAL ARTICLE
Governance of Direct‐to‐User Digital Mental Health Tools: Emphasizing Transparency over Paternalism.
Published In: Hastings Center Report, 2025, v. 55, n. 3. P. 29 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Panda, Om D.; Binkley, Charles E. 3 of 3
Abstract
Digital mental health tools are increasingly used outside traditional clinical settings, creating an engagement paradigm beyond the existing regulatory scope, as noted by Amitabha Palmer and David Schwan in their article "Digital Mental Health Tools and AI Therapy Chatbots: A Balanced Approach to Regulation." Introducing the direct‐to‐user concept (which concerns individuals as autonomous agents navigating self‐regulation, enhancement, and meaning making), we propose a shift from paternalism and rigid standards critiqued by Palmer and Schwan toward a human‐centered governance approach in which radical transparency, individual agency, and shared accountability are themselves the standards. Transparency enables informed choice through intelligible disclosure of data, validity, and incentives, which empower users to assess trade‐offs based on personal goals and values. Evolving accountability frameworks, such as voluntary certification with collective liability, reinforce the scalability and ethics of this model, which can also be broadly applied to other digital health tools and cognitive‐enhancement technologies. This governance framework fosters individualized, participatory ecosystems to make this new generation of tools more accessible. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Hastings Center Report. 2025/05, Vol. 55, Issue 3, p29
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:0093-0334
- DOI:10.1002/hast.5009
- Accession Number:186162339
- Copyright Statement:Copyright of Hastings Center Report is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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