Human Agency, Causality, and the Human Computer Interface in High-Stakes Artificial Intelligence

Current discourse on Artificial Intelligence (AI) ethics, dominated by "trustworthy" and "responsible" AI, overlooks a more fundamental human-computer interaction (HCI) crisis: the erosion of human agency. This paper argues that the primary challenge of high-stakes AI systems is not trust, but the preservation of human causal control. We posit that "bad AI" will function as "bad UI," a metaphor for catastrophic interface failures that misrepresent system state and lead to human error. Applying Marshall McLuhan's media theory, AI can be framed as a technology of "augmentation" that simultaneously "amputates" the user's direct perception of causality. This places the interface as the critical locus where a "double uncertainty"--that of the human user and that of the probabilistic model--must be mediated. We critique current Explainable AI (XAI) for its correlational focus and failure to represent uncertainty. We conclude by proposing a rigorous, nested Causal-Agency Framework (CAF) that integrates causal models, uncertainty quantification, and human-centered evaluation to restore agency at the interface.