Ethical AI Use Dataset

A disability-informed architecture for ethical AI collaboration, and trauma-aware advocacy

Motif Mapping is a conceptual counseling dataset designed by Andrew Horman to center lived experience, narrative sovereignty, and symbolic integrity in mental health practice and adjacent AI collaboration. It integrates verified definitions from trauma-informed care, narrative therapy, strengths-based theory, and disability justice — while explicitly excluding diagnostic simulation or unsupervised therapeutic use.

This dataset is not a clinical tool, but a symbolic and ethical scaffold for:

• 🔒 Ethical AI Integration: Includes memory filters, scope boundaries, and fidelity checklists to prevent misuse or metaphorical harm
• 📚 Public Access and Advocacy: All resources are paywall-free, citation-verified, and formatted for inclusive education and systemic dialogue
• 🛠️ Adaptation Safeguards: Features an adaptation audit tool and symbolic ethics license to protect against cultural appropriation and symbolic erasure
• 🧠 Conceptual Use Only: Designed for supervised collaboration, curriculum development, and public-facing education — not diagnosis or treatment

The dataset includes:

• Verified modality definitions
• Symbolic ethics guide and gallery
• Scope boundaries and memory filters
• Licensing and attribution protocols
• Resource curation logic and accessibility statement
• Feedback systems and fidelity checklist
• Packaging tools for AI platforms, course integration, and public distribution