PAIR 1 — LAW AND ALGORITHMIC RISK ASSESSMENT Passage A Courts sometimes use algorithmic risk assessments when deciding bail or sentence conditions. Because an assessment may affect a person's liberty, defendants should be able to examine the model's rules. Vendors often resist disclosure by invoking trade-secret protection, but ordinary commercial secrecy should not outweigh the need to challenge evidence used by the state. A model may assign importance to variables that function as proxies for race or poverty, and a defendant cannot identify such a problem from the final score alone. Limited disclosure under a protective order would reduce commercial harm while allowing independent experts to test the model's assumptions. Procedural fairness requires more than knowing that software produced a number; it requires a meaningful opportunity to question how that number was produced. Passage B Releasing a risk model's source code may appear to promote transparency, yet code inspection alone can create a false sense of assurance. Harm often arises from the data used to train or operate a model: arrest records may reflect unequal policing, and missing information may be concentrated in particular communities. Even perfectly readable code will not reveal every such defect. Courts should therefore require continuing outcome audits that compare error rates across relevant groups, document data quality, and test performance after deployment. Technical experts may inspect code when a specific dispute makes that necessary, but routine oversight should focus on how the system actually behaves. A model that is elegant in theory but unreliable in practice is not made fair merely because its instructions are visible. QUESTION 2: Both authors would most likely agree that

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