Defending the Hierarchical Result Models of Precedential Constraint

Explores methods for defending hierarchical models that represent precedential constraints, relevant for robust legal reasoning and AI systems in jurisprudence. This research offers valuable insights for developing more reliable and fair AI applications in the legal domain, addressing critical issues of consistency and justification in automated decision-making.