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

2026-04-14

AI Summary

This study addresses how design flaws in high-risk artificial intelligence systems often undermine human agency, leading to a loss of causal control and increased human error. To overcome the limitations of existing explainable AI approaches—which predominantly focus on associative patterns rather than causal understanding—the work proposes a nested Causal-Agency Framework (CAF). Integrating causal modeling, uncertainty quantification, and human-centered evaluation, this framework uniquely incorporates McLuhan’s media theory to reframe the role of AI interfaces as mediators of human perception and action. By enhancing causal transparency and jointly managing epistemic and aleatoric uncertainties, CAF reconstructs agency-preserving mechanisms within human-AI interaction, offering a novel paradigm that supports human causal reasoning and control in high-stakes AI applications.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Reasoning under Uncertainty: Causality
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

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.

Problem

• Human Agency
• Causality
• Human-Computer Interface
• High-Stakes AI
• Uncertainty

Innovation

• human agency
• causal control
• human-computer interface
• uncertainty quantification
• Causal-Agency Framework