The world is becoming more and more reliant on the use of Artificial Intelligence (AI). This goes beyond Tik Tok’s predictive algorithm, or Netflix recommending other shows you may like; AI is taking over media you consume, from news to entertainment. It can be perilous to rely on media sources that promote AI-generated material, as there are no fact checkers, or really any sort of quality control. Generative AI programs tend to regurgitate whatever is available to it, that semi-follows the prompts its given, with no way to assess if the data it presents to the reader is reliable or just creating and/or perpetuating fallacies.
The following prompt was given to ChatGPT:
Please write a 400-700 word blog post describing how does existing policy related to improving nutritional status in individuals experiencing food insecurity incorporate evidence, and the opportunities to incorporate research and other evidence to innovate and advance policy related to improving nutritional status in individuals experiencing food insecurity.
This is the product it returned:
Advancing Nutrition Policy for Food-Insecure Populations: From Evidence to Innovation
Food insecurity remains a persistent public health challenge in the United States, closely linked to poor diet quality, chronic disease, and adverse developmental outcomes across the life course. Existing nutrition policies—particularly federal food assistance programs—are grounded in a substantial body of scientific evidence. However, opportunities remain to better integrate emerging research, improve policy design, and advance nutritional outcomes for vulnerable populations.
Evidence-Based Foundations of Current Policy
The cornerstone of U.S. food insecurity policy is the Supplemental Nutrition Assistance Program (SNAP), complemented by programs such as the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). These programs are explicitly designed around evidence demonstrating the relationship between income support and improved food access.
A robust body of research shows that SNAP significantly reduces food insecurity. Participation has been associated with reductions of up to 30% in overall food insecurity, with even greater effects among children and households experiencing severe deprivation. Longitudinal and quasi-experimental studies further demonstrate improvements in health outcomes, including reductions in low birth weight and better overall child health status among SNAP participants. Similarly, WIC participation is associated with improved maternal nutrition, reduced infant mortality, and better developmental outcomes.
Importantly, evidence suggests synergistic effects when programs are combined. Participation in both SNAP and WIC has been shown to increase food security more than participation in SNAP alone. These findings illustrate how current policies incorporate evidence through program design, eligibility criteria, and targeted populations (e.g., pregnant women, children, and low-income households).
In addition to improving food access, these programs are associated with broader social benefits. For example, participation in SNAP and WIC is linked to reduced poverty, improved long-term food security trajectories, and even decreased involvement with child welfare systems. Together, these outcomes reflect a policy framework grounded in interdisciplinary evidence spanning nutrition science, economics, and public health.
Opportunities to Strengthen Evidence Integration
Despite strong evidence supporting existing programs, gaps remain in how policy incorporates emerging research—particularly regarding diet quality and health equity. While SNAP effectively reduces food insecurity, its impact on nutritional quality is more complex. This highlights an opportunity to better align policy with nutrition science by integrating incentives for healthy food purchases or disincentives for low-nutrient items.
Innovative policy approaches could include expanding produce incentive programs, integrating medically tailored meals, and leveraging behavioral economics to guide healthier choices. Research suggests that targeted incentives and restrictions—such as limiting sugar-sweetened beverages—may improve dietary quality and reduce chronic disease risk. However, such policies must be carefully designed to avoid stigmatization and unintended consequences.
Another critical opportunity lies in improving data infrastructure. The discontinuation or limitation of national food insecurity data collection threatens the ability to evaluate policy effectiveness and guide evidence-based decision-making. Strengthening surveillance systems and incorporating real-time data analytics would allow policymakers to more rapidly assess program impact and adapt interventions.
Innovation Through Interdisciplinary and Equity-Focused Policy
Future policy innovation should emphasize interdisciplinary collaboration and equity. Structural barriers—such as transportation, digital access, and program enrollment gaps—limit participation among eligible populations. Addressing these barriers through community-based outreach, digital tools, and simplified enrollment processes represents a key opportunity to translate research into practice.
Additionally, integrating healthcare and nutrition policy offers a promising avenue for innovation. Programs that screen for food insecurity in clinical settings and connect patients to food resources (e.g., “Food is Medicine” initiatives) align with growing evidence linking nutrition to health outcomes. Expanding these models could improve both clinical and population health outcomes.
Finally, policy must increasingly incorporate culturally relevant and community-informed approaches. Research demonstrates that one-size-fits-all interventions are less effective than those tailored to the needs of specific populations, particularly marginalized communities disproportionately affected by food insecurity.
Conclusion
Existing nutrition policies addressing food insecurity are strongly grounded in evidence demonstrating their effectiveness in improving food access and health outcomes. However, advancing these policies requires deeper integration of emerging research on diet quality, equity, and system-level innovation. By enhancing data systems, leveraging interdisciplinary approaches, and prioritizing community-centered design, policymakers can move beyond reducing hunger to promoting optimal nutritional health for all (OpenAI, 2025).
At first read, the AI-generated product sounds rather impressive, however, as you continue to process the information, the reader will discover that there are a lot of words (680) that don’t really say anything with any depth.
The material, for the most part, is rather abstract. Generative AI makes statements without supporting them. It makes the claims that there is “robust” evidence or “substantial” research, but does not give one example of anything robust nor substantial.
This particular AI-generated blog post was not inaccurate, per se, but more like it was rather incomplete.
Even when introducing the topic, AI is unable to describe food insecurity with any semblance of specificity. It sounds as if AI defines food insecurity as the lack of access to food, by the way it starts referencing the Supplemental Nutrition Assistance Program (SNAP) and the Women, Infants, and Children program (WIC). Although, from reading previous posts on this blog, we have learned that food insecurity is not merely the access to food, but food that is nutritious, adequately meets our energy expenditures, and aligns with our dietary restrictions. In addition, food security also encompasses the knowledge and ability to prepare food, and the time needed to make it happen.
In another paragraph, the AI-generated content refers to taking an interdisciplinary approach when drafting future policy, but neglects to mention what disciplines should be consulted. The entire AI-generated blog post pretty much ignores the part of the prompt that talks about improving nutritional status, which is an entire half of the policy topic in question.
AI has the buzz words to pique interest, but lacks the complexity to paint a clear and accurate picture, or give a complete narrative of what it is prompted to explain.
There are some potential benefits to using generative AI in the context of improving nutritional status in those who are experiencing food insecurity. That includes potentially coming up with meal plans that address different health issues, then prompting AI to use the most affordable ingredients, so as to maximize SNAP benefits.
A huge risk is that generative AI is only as good as the data it prowls through. Bad data can lead to dangerous recommendations. People may potentially alter their diets or make decisions that impact their health or medical care based on information they receive from AI sources, which are not overseen by healthcare professionals of any sort.
A potential policy approach that might be incorporated to capitalize on benefits and mitigate against risks would be to insist on the development of a computer code that required generative AI programs to only source their data from reputable, scholarly databases for questions regarding health and medical care.
With the acknowledgement that this may be a difficult feat due to article inaccessibility behind paywalls, and generative AI’s predilection for creating its own official sounding, reputable sources (I’ve checked the sources its listed on different tasks I’ve given it, and the frequency with which it creates phantom sources is rather staggering), this proposition is not so far-fetched that it couldn’t be achieved with reasonable expenditure from the consumer.
What price would you put on authentic, corroborated, legitimate data?
Reference
OpenAI. (2025). ChatGPT (August 2025 version). [Large language model]. https://chatgpt.com/