Sherlin and Longo propose an AI ethics framework for neuroregulation practice, pairing a three-dimension risk continuum of opacity, clinical consequence, and distance from oversight with a five-element clinical policy table

The article proposes a risk continuum model organizing AI applications along three dimensions: opacity, clinical consequence, and distance from oversight, to stratify AI use in neuroregulation practice. No ethics code specific to neuroregulation had yet addressed automated qEEG analysis, protocol recommendation, AI-assisted documentation, and consumer-facing mental health applications directly; the model places these scattered uses on a single comparable risk coordinate. Conceptual and practical framework building, derived from established ethics codes and literature; the text reports no empirical data or validation study.

The article organizes AI ethics concerns into four domains: informed consent and transparency, practitioner competence and scope, data privacy and vendor accountability, and three AI accuracy risks of hallucination, automation bias, and collusion. It consolidates consent, competence, privacy, and accuracy issues that are usually discussed separately into one checklist for neuroregulation practitioners, and names collusion as a distinct accuracy risk. A conceptual synthesis anchored in the BCIA, ISNR, APA, and ACA ethics codes and the APA (2025) AI ethical guidance; it is normative argument rather than empirical test.

The article presents a five-element practice policy framework in tabular form for direct clinical use. It converts ethical principles into actionable policy elements so practitioners need not derive operational steps from abstract codes themselves. The framework is presented as a table, a practice-oriented tool; the text reports no implementation or outcome evaluation in real settings.

The article argues that existing ethics principles are sufficient to guide responsible AI integration when applied deliberately, that the practitioner remains accountable for AI-assisted decisions, and that proactive policy development positions neuroregulation clinicians to shape the field's emerging standards. It places accountability firmly on the practitioner rather than letting it shift with tool design or vendor claims, and emphasizes proactive rather than reactive response. A normative position statement derived from existing codes; the text offers no empirical support.

The framework is meant for neuroregulation, neurofeedback, and qEEG practitioners and their organizations, to be used when adopting automated qEEG analysis, protocol recommendation systems, AI-assisted documentation, and client-brought consumer mental health applications, for risk stratification, informed consent communication, defining competence and scope, arranging data privacy and vendor accountability, and drafting organizational policy from the five-element table. It applies to translating existing ethics codes into daily decisions and written policy, not to evaluating the performance of any specific AI tool.

The text read here is an incomplete version containing only the abstract, keywords, references, and licensing information; the details of the risk continuum model, the full argument for the four ethical domains, and the specific contents of the five-element policy table do not appear in the loaded text, so their item structure and argumentative detail cannot be summarized here, which is an open question to confirm by opening the original. In addition, the article is a conceptual framework, so the usability and acceptance of its risk stratification and policy table in real clinical organizations, and how practitioner accountability for AI-assisted decisions would be implemented as vendor claims and tool designs keep changing, remain open to later practice and research.