Determining chemical risk and the need for mitigations in food is complex. This session will provide insight into how leaders from various industries are addressing this challenge as it relates to heavy metals in foods, and how a regulatory perspective can be used to benchmark the approach taken. This session will explore how data is used to drive a science-based approach to develop this insight and inform best practices for all stakeholders. The two key factors for evaluating chemical risk in food are the toxicity of the chemical and the amount that is consumed. Toxicity is defined through a multitude of studies, and once characterized, tends to be stable. However, the amount consumed, or the exposure, is a very dynamic variable. Factors in food production and processing significantly impact the concentration of heavy metals in food, as do the consumption habits and practices of a population, including the type of food being consumed, age, socioeconomic factors, etc. Hence, this makes risk dynamic and even more challenging in developing federal policies and identifying practical mitigation measures. One solution to this is collating large sets of data from multiple sources into a dynamic exposure assessment model that calculates the exposure, ranked by high consumers and low consumers over time. These consumption habits can be compared with the nutritional benefits of a variety of diets. Models are never perfect, however, there are many techniques that are used to check and validate assumptions made and to ever improve and refine the accuracy of the output. The panel participants include representatives from the fresh produce industry, the consumer goods industry, the US FDA and experts in data science. Together they will discuss the approaches they are taking to tackle this significant challenge. Speakers: Sandrine Pigat, Emily Moyer, PhD