On the heels of Upstate New York’s first official heatwave of 2026, new Cornell-led research may help emergency managers answer a question that will only become more urgent as extreme heat becomes more common: Can artificial intelligence help cities respond faster and smarter when temperatures become dangerous?
The answer, according to researchers, depends on the job.
A Cornell-led team found that AI-based tools may be better suited for fast-moving decisions during heat emergencies, such as targeting outreach, issuing alerts, or responding to power outages. But simpler, human-readable index scores may still work better for long-term planning and public-facing explanations of heat vulnerability.
The research focused on New York City’s Heat Vulnerability Index, a tool used in planning efforts related to cooling strategies, urban forests and neighborhood-level heat risk. The study found that those kinds of indices can be useful, but also sensitive to small changes in data inputs and policy priorities.
That matters beyond New York City. Across Upstate New York, where many communities just came through days of high heat, humidity and elevated heat index values, local governments face many of the same questions: Who is most at risk, where should resources go, and how quickly can officials reach vulnerable residents when the weather turns dangerous?
Heat emergencies are becoming harder to manage
Extreme heat is not just a big-city problem.
In Upstate New York, heat emergencies can strain older housing, rural transportation systems, volunteer emergency services, aging electric infrastructure and communities where residents may not have reliable air conditioning. The first official heatwave of 2026 underscored how quickly uncomfortable weather can become a public health issue.
When temperatures climb into the 90s, emergency officials need to make decisions quickly. They may need to open cooling centers, communicate with older adults, check on medically vulnerable residents, monitor power outages, and coordinate with schools, nonprofits or local service agencies.
The Cornell study looked at whether those decisions are better guided by straightforward index-based tools or more complex predictive algorithms.
Researchers found there is no single answer.
Jennah Gosciak, a doctoral student in information science and lead author of the study, said the team wanted to compare AI-based tools not just against abstract standards like bias or fairness, but against the simpler index systems governments already use.
Those index tools are common in policymaking because they are relatively easy to understand. They take a set of inputs, produce a risk score, and give officials a way to compare one neighborhood or area against another.
But the simplicity comes with limits.
Simple heat-risk scores have strengths and weaknesses
New York City’s Heat Vulnerability Index uses five inputs: daytime summer surface temperature, household air conditioning access, vegetative cover, median household income, and the percentage of residents who are non-Latino Black.
The index produces a risk score from 1 to 5.
Cornell researchers compared the city’s index with two other widely used tools: the Federal Emergency Management Agency’s National Risk Index and the Centers for Disease Control and Prevention’s Heat and Health Index.
They found that the Heat Vulnerability Index can shift significantly depending on inputs and goals. In other words, the results can change based on whether officials prioritize health outcomes, economic loss, infrastructure concerns, or other policy objectives.
That does not mean index tools are useless. It means officials need to understand what those tools actually measure.
For long-term planning, index scores can be valuable. They can help governments identify neighborhoods that need more trees, cooling infrastructure, public investment, or outreach planning before the next heat emergency arrives.
They are also easier to explain to the public. A simple score based on visible conditions, household data and neighborhood characteristics can help residents understand why one area may face higher heat risk than another.
But during an active heat emergency, a static index may not move fast enough.
AI may help with real-time decisions
The Cornell team found that predictive algorithms may be more useful when officials need to act quickly during changing conditions.
That could include decisions about where to send outreach teams, where to prioritize cooling-center information, or how to respond when a power outage overlaps with high heat.
Those are on-the-fly decisions. They may require live or frequently updated data, including weather conditions, outage reports, emergency calls, hospital activity, transportation access, or neighborhood-level vulnerability.
AI tools can process complex and changing inputs faster than a traditional index. That makes them potentially useful for emergency operations.
But they come with their own problems.
Algorithms can be harder for the public to understand. They may rely on high-level data that residents never see. They can also produce outputs that vary sharply when inputs change, which creates concerns about transparency, trust and accountability.
That trade-off is the heart of the Cornell research.
A simple index may be easier to explain, but less useful in fast-moving situations. An AI tool may support faster decisions, but it may be harder to justify publicly.
For local governments, especially smaller communities with limited staff, the practical question is not whether AI is better. It is whether the tool matches the decision being made.
What it could mean for Upstate communities
The timing of the research is notable for New York.
As the first heatwave of 2026 fades, communities across the Finger Lakes, Central New York and the Rochester region are already looking ahead to the next stretch of dangerous heat. Extreme heat planning is no longer just about weather forecasts. It is about public health, infrastructure, transportation, housing and emergency communication.
Cornell’s research suggests local governments should be careful about using one tool for every heat-related decision.
For long-range planning, an index may help identify which neighborhoods need tree cover, cooling access, or resilience funding. For emergency response, predictive tools may help officials make faster decisions as conditions change by the hour.
The researchers identified several trade-offs for decision-makers, including the scope of the problem, the timing of the decision and the intended audience. A tool used to decide where to plant trees may not be the right tool to decide where to send an emergency alert during an outage.
That distinction matters in Upstate New York, where city, village, town and county governments often operate with fewer resources than New York City. Many do not have large emergency management teams or advanced data systems. Any tool they use needs to be practical, explainable and reliable under pressure.
The Cornell study does not argue that AI should replace human judgment. It argues that officials should understand the strengths and limits of both AI-driven algorithms and simpler risk indices.
That may become increasingly important as heat emergencies become more frequent, more localized and more dangerous.
The research, titled “Scrutinizing Index-Based Risk Assessments: A Case Study in NYC Decision-making for Heat Emergency Management,” was presented at the ACM Conference on Fairness, Accountability, and Transparency in Montreal. The senior author is Allison Koenecke, assistant professor of information science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science.
Other co-authors include Angelina Wang, assistant professor of information science at Cornell Tech and Cornell Bowers, and Luke Boyce, manager of the Strategic Initiatives Program at New York City Emergency Management.
For Upstate New York, the takeaway is straightforward: better heat planning will not come from technology alone. It will come from matching the right tool to the right decision before the next heatwave arrives.
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