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Algorithm fairness in the application of artificial intelligence (AI) isessential for a better society. As the foundational axiom of social mechanisms,fairness consists of multiple facets. Although the machine learning (ML)community has focused on intersectionality as a matter of statistical parity,especially in discrimination issues, an emerging body of literature addressesanother facet -- monotonicity. Based on domain expertise, monotonicity plays avital role in numerous fairness-related areas, where violations could misguidehuman decisions and lead to disastrous consequences. In this paper, we firstsystematically evaluate the significance of applying monotonic neural additivemodels (MNAMs), which use a fairness-aware ML algorithm to enforce bothindividual and pairwise monotonicity principles, for the fairness of AI ethicsand society. We have found, through a hybrid method of theoretical reasoning,simulation, and extensive empirical analysis, that considering monotonicityaxioms is essential in all areas of fairness, including criminology, education,health care, and finance. Our research contributes to the interdisciplinaryresearch at the interface of AI ethics, explainable AI (XAI), andhuman-computer interactions (HCIs). By evidencing the catastrophic consequencesif monotonicity is not met, we address the significance of monotonicityrequirements in AI applications. Furthermore, we demonstrate that MNAMs are aneffective fairness-aware ML approach by imposing monotonicity restrictionsintegrating human intelligence.

Monotonicity for AI ethics and society: An empirical study of the  monotonic neural additive model in criminology, education, health care, and  finance