Restoring the public’s trust in the pandemic recovery era has become a defining challenge for public health professionals. COVID-19 fundamentally reshaped the public’s expectations for health information, as what once lived quietly inside surveillance systems and epidemiology reports suddenly became part of daily public life. Millions of people routinely monitored dashboards, scrutinizing case counts and attempting to interpret hospital data they had never considered before. Public health professionals have long known the importance of transparency, but the pandemic highlighted just how tightly transparency is woven into public trust.

The realities of data collection and reporting during the peak of the pandemic were complex. Reporting standards varied widely across jurisdictions. Definitions and recommendations evolved as new information became available. Data delays and inconsistencies became magnified in the public eye, fueling questions, confusion, and doubt. In many cases, speculation and misinformation quickly filled information gaps, which did more than disrupt communication, it also eroded confidence in the data and the institutions behind it. 

Now, the rapid rise of artificial intelligence (AI) is transforming the data landscape again. With its insurgence, AI has captured public attention both positively and negatively. While we’ve seen its improvements in the medical sciences and how it can boost productivity by managing administrative tasks, there are also major concerns about data privacy, the danger of deep fakes, and the debate over environmental impact, as some data centers consume 5 million gallons of water every day.

For public health, the applications of AI are limitless. Algorithms can model outbreaks, analyze community trends, and draft communication materials. In a field that has been chronically challenged with staffing shortages and limited resources, the promise of AI is immense, though uptake has been slow. According to the National Association of County and City Health Officials’ (NACCHO’s) 2024 Public Health Informatics Profile, only five percent of local health departments (LHDs) shared that they were currently using AI and machine learning to improve their processes or efficiency and 84 percent of LHDs had no plans to use AI in the coming year.

Large LHDs were three times more likely to report using AI than small and medium LHDs, and most agencies cited using it to develop communication materials or plans. Notably, the more than 70 percent of LHDs that were not currently using AI expressed some degree of interest in using it in their work, with interest substantially higher for urban LHDs than among their rural counterparts (84 percent and 59 percent, respectively).

Public health now sits at another inflection point. AI models and the data they rely on are often opaque to those who use them and even more so to the communities affected by their results. Public health professionals are increasingly asked to explain how models work, what data they rely on, what biases may be built into them, and how the results should and should not be interpreted. As these algorithms become more integrated into public health workflows, the need to communicate limitations and model uncertainty becomes even more crucial.

Rebuilding and strengthening trust in this environment requires deliberate, sustained effort. Clear communication and openness about data limitations and methodology choices must become common practice, even when those explanations are complex. Public health agencies must adopt standardized reporting practices that make data accessible without overwhelming the public or compromising privacy. This journey toward rebuilding trust also means engaging communities more deeply. Trust is not restored through one-way communication; it’s built through honest dialogue, genuine listening, and incorporating community perspectives into how data systems and AI tools are designed and implemented. When people feel included, they feel more confident in the systems that serve them.

The pandemic altered the public’s relationship with health data, and AI is transforming it again. The path forward requires public health institutions to embed transparency into every layer of their work, not only in the data they share, but also in how they use data-driven tools to make decisions. Ultimately, transparency will not only strengthen trust but also fortify the resilience and legitimacy of public health systems overall.

About the Author

Nichole Fusilier

Nichole Fusilier, MS, MPH, is a researcher at the Center for Public Health Systems. Their expertise includes evaluating community mental health programs, analyzing the impact of healthcare policies, and conducting research aimed at improving community health outcomes. Nichole is committed to advancing the well-being of individuals and their communities and reducing health disparities through research and policy advocacy.

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