 To avoid some of the difficulties described above, other models rely on relevant background information, for example, geographical information about people’s opportunities to commit crimes (Fenton et al. 2019). But even if these models are successful in giving well-informed assessments of prior probabilities, a deeper difficulty lingers. That is, any assessment of prior probabilities, no matter how it is done, is likely to violate existing normative requirements of the trial system (Dahlman 2017; Engel 2012; Schweizer 2013). If the assessment of prior probabilities relies on demographic information, people who belong to certain demographic groups will be regarded as having a higher prior probability of committing a wrong than others. But if some people’s priors are higher than other people’s priors, it will be easier to convict or find liable those who are assigned higher priors, even if the evidence against them is the same as the evidence against those assigned lower priors. This outcome can be seen as unfair, especially in criminal cases, since it exposes to a higher risk of conviction those innocents who are assigned a higher prior probability because of the demographic group they belong to (Di Bello and O’Neil 2020).