Many prevention programs are launched based on assumptions, best intentions, or limited evidence. DIG is designed to evaluate whether a proposed intervention is likely to reduce risk, whether sufficient evidence exists to support action, and whether unintended consequences could outweigh expected benefits. The goal is to improve decision quality before resources, time, and trust are invested.

Not all interventions eliminate risk. Some simply move risk from one area to another. DIG evaluates both the original problem and the potential consequences introduced by the intervention itself. This helps organizations understand whether a prevention strategy creates a net improvement or merely shifts exposure elsewhere within the system.

Yes. One of the primary objectives of DIG is determining whether available evidence is adequate to support a decision. In some situations, the most defensible conclusion may be that additional information is needed before action is taken. This helps organizations avoid costly initiatives based on incomplete, low-quality, or non-admissible evidence.

AI systems can generate recommendations rapidly, but they often do not explicitly evaluate evidence sufficiency, intervention risk, or uncertainty. DIG provides a structured framework for assessing objectives, evidence quality, competing risks, expected outcomes, and confidence levels before recommendations are acted upon. This helps create more transparent, auditable, and defensible decision processes for both human and AI-assisted environments.