DARE: An Explainable AI-visualization Framework for Ill-defined Decision Making

Abstract

Real-world decision making often unfolds in fluid, uncertain, and ill-defined contexts where objectives shift, data are incomplete, and non-quantifiable factors such as social values, ethics, and institutional constraints play critical roles. Conventional AI and decision-support systems assume fixed criteria and stable data, leaving these contexts underserved. Building on an interdisciplinary definition of decision making attentive to its ill-defined forms, we introduce DARE, an explainable AI and visualization framework that complements the FAIR data principles with the DARE principles: Deliberation, Agency, Resilience, and Empathy, which emphasize dialogue, human control, adaptability, and human sensitivity in design. DARE conceptualizes decision making as an iterative alignment of human-defined criteria with algorithmic representations through which decision structure gradually emerges. We revisit existing AI paradigms through this lens and illustrate how weak supervision and concept-based modeling exemplify this process by connecting heuristic human reasoning to interpretable model concepts. Input visualization serves as the expressive layer that captures evolving, qualitative, and uncertain reasoning through interaction, allowing humans to externalize and refine decision logic before formalization. Explainability in DARE arises not from post-hoc justification but from the continuous visibility of how human and algorithmic reasoning co-develop. Uncertainty is treated as an inherent dimension of deliberation, something to represent, navigate, and learn from within the decision process, while human and algorithmic heuristics are regarded not as truths or biases but as evolving hypotheses to examine and refine through interaction. Together, these elements support human–AI decision making that remains transparent, adaptable, and grounded in human judgment across value-laden and ill-defined contexts.