A Cornell University-led research team has demonstrated that neither artificial intelligence predictive algorithms nor traditional human-readable indices hold a definitive advantage for urban heat emergency management, with the optimal choice contingent upon specific decision-making contexts and target audiences. The findings were presented at the ACM Conference on Fairness, Accountability, and Transparency in Montreal between June 25 and 28, underscoring a critical shift in how policymakers evaluate risk-assessment tools during climate-driven crises. Led by doctoral candidate Jennah Gosciak and supervised by information science professor Allison Koenecke, the study examined New York City’s Heat Vulnerability Index, a five-metric scoring system that factors in daytime surface temperatures, air conditioning access, vegetative cover, median household income, and demographic composition to generate risk assessments ranging from one to five. The research team benchmarked the HVI against the Federal Emergency Management Agency’s National Risk Index and the Centers for Disease Control and Prevention’s Heat and Health Index, while also evaluating the performance of comparable predictive AI models. The analysis revealed that both indexing methods and machine learning algorithms exhibit high sensitivity to minor fluctuations in input data, producing substantially different outputs even when variables shift marginally. Crucially, the researchers concluded that simplicity and interpretability do not inherently trump predictive accuracy. Human-based indices proved more effective for long-range strategic planning and assessing abstract socio-environmental concepts like community heat vulnerability, whereas AI-driven models demonstrated superior utility for time-sensitive operations, including real-time emergency alerts, resource routing, and targeted public outreach. Rather than advocating for wholesale replacement of existing frameworks, the study proposes a nuanced evaluation framework outlining seven distinct trade-offs for municipal decision-makers. The findings challenge the prevailing bias toward algorithmic superiority in public sector technology adoption, emphasizing that tool selection must align with operational goals, funding allocation requirements, and stakeholder communication needs. Gosciak emphasized that existing indices remain entrenched in environmental justice initiatives and resource distribution policies, making comparative analysis essential rather than discarding proven methodologies in favor of opaque models. The methodological approach established in this New York City case study carries immediate relevance for climate adaptation strategies globally. As extreme temperature events intensify, urban planners and emergency management agencies are increasingly pressured to adopt data-driven responses. The research provides a structured pathway for integrating both traditional indices and advanced predictive systems, ensuring that technological deployments in crisis management enhance rather than complicate public safety outcomes. Municipal authorities seeking to optimize heat emergency protocols are now equipped with a validated framework for matching tool capabilities to specific operational demands, balancing transparency with predictive performance in an era of rapid climate escalation.