Multi-Criteria Comparison as a Method of Advancing Knowledge-Guided
  Machine Learning
Jason L. Harman Jaelle Scheuerman
This paper describes a generalizable model evaluation method that can be
adapted to evaluate AI/ML models across multiple criteria including core
scientific principles and more practical outcomes. Emerging from prediction
competitions in Psychology and Decision Science, the method evaluates a group
of candidate models of varying type and structure across multiple scientific,
theoretic, and practical criteria. Ordinal ranking of criteria scores are
evaluated using voting rules from the field of computational social choice and
allow the comparison of divergent measures and types of models in a holistic
evaluation. Additional advantages and applications are discussed.