This analysis highlights the need to prioritize human-system interaction in AI governance, indicating a shift in focus from model internals to user interfaces.
Key Points
The paper aims to reframe AI governance by emphasizing the importance of the interaction boundary over model internals.
Analyzed recent AI governance failures, including the Bixonimania incident.
Explored the concept of the Eliza Effect in the context of AI systems.
Argued for a shift in focus from model weights to governance surfaces.
Identified the interaction boundary as the primary site of governance issues.
Showed that past governance failures stem from misidentified surfaces rather than model flaws.
Established the Eliza Effect as crucial for understanding human interaction with AI systems.
Cite This Study
Narnaiezzsshaa Truong (2026) studied this question.