The Uncertainty in Crime Risk Governance

Public safety leaders operate in a world of uncertainty. Their job is not simply to be right all the time but to make informed decisions that maximize the odds of success. ​

This principle, explored extensively by renowned psychologists Amos Tversky and Daniel Kahneman, highlights a fundamental challenge: human beings are not naturally inclined to think in probabilities, yet public safety depends on assessing risks effectively before acting.

​Every day, police chiefs and municipal decision-makers must anticipate threats, minimize uncertainty, and allocate resources efficiently to prevent and mitigate crime. Their actions are scrutinized by their peers, elected officials, and local residents, making it essential that crime risk governance be as precise and defensible as possible.

Risk assessment involves evaluating the probabilities of particular outcomes. Since risk is probabilistic, the extent to which police can minimize uncertainty directly influences how well they judge crime threats and their consequences. Human judgement is required to act wherever there's uncertainty and the opportunity for human fallibility exists wherever there's judgement. Using data analytics to reduce uncertainty while also acknowledging what we know we don't know helps determine the confidence level in what we do know.

At some point, even the most data-driven, artificially intelligent predictions must invite human judgment into the decision-making process. Crime risk governance is the moment when analytics is considered alongside other pieces of information to make informed judgments about managing crime risks with the greatest odds of short- and long-term success. Here, success includes solving crime problems while also ensuring solutions align with local needs and community expectations -- which adds to the legitimacy of actions and the sustainability of impacts.

So, to deploy resources, implement intervention programs, and otherwise act with confidence, we must acknowledge the inherent limitations of even the most experienced human intuitions due to uncertainties in decision-making. In such a context, a fundamental goal of crime analysis is to reduce as much uncertainty as possible. This is achieved by synthesizing insights from human judgment, data, and empirical analyses through structured processes that inform decision-making and guide crime prevention programming and risk reduction efforts. This process is realized with Data-Informed Community Engagement, a systematic approach that integrates Risk Terrain Modeling (RTM) analytics with real-world contexts to enhance public safety outcomes for everyone.