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Judges in family court and in child protective proceedings evaluate huge amounts of information in determining what is in the best interests of a child. Deciding matters such as child support, residency, contact, orders of protection, timelines for family reunification, and termination of rights is an onerous task. Courts have time constraints, judges are vulnerable to natural human biases, and most parties in family court are unrepresented. Additionally, it is unlikely that judges will have all the necessary information about the parties, since the whole "story" of the case is told within the confines of the courtroom. What if there were a tool that enhanced magistrates' and judges' ability to assess risk and predict parties' behavior? Predictive Risk Modeling (PRM) in family court and protective court proceedings might be a way to create a fuller picture of a parent or guardian's risk to a child in their care. PRM uses data patterns from historical behavior and life events to identify predictors of risk. PRM algorithms then assign risk categories to individuals. These risk categories can be condensed into scores that suggest the likelihood that an individual will or will not behave in a certain way in the future. There are notable areas where this technology is being used successfully: front line child welfare work, assessment of persons accused of intimate partner violence, and bail setting. Bringing PRM to family courtrooms would require careful procurement, transparency, rigorous methodology and impact evaluations, and an ethical review. The algorithms could also be tailored to the jurisdiction. While judges are free use scores at their discretion, the presumption is that the score would be considered, but not conclusive. If these requirements were satisfied, PRM scores promise to be a way to increase the thoroughness and efficiency of selected family division and protective court proceedings.