Semantic trail
A body of work on how to describe human-AI collaboration honestly — from the original taxonomy through the argument that attribution should be process evidence, not disclosure language.
Most AI acknowledgments are too vague to be useful. Process transparency gives teams a practical, auditable way to describe human-AI work without pretending the model is an author.
Current AI attribution approaches fail to address real-world adoption challenges. While industry solutions like IBM's toolkit offer important first steps, they miss the social and professional dynamics that shape practice across different professional contexts. Drawing from my experience integrating GenAI across multiple courses and developing systematic attribution practices, here's what's missing from current research.
An independently developed framework proposing standardized transparency protocols for human-AI collaboration across journalism, academic, and creative professional domains. Developing practical framework with four attribution categories positioned for adoption similar to Creative Commons licensing model.
If there's a missing connection this path should include, or something new you'd like to see explored here, let me know.
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