Algorithm fairness in the application of artificial intelligence (AI) is
essential 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 addresses
another facet -- monotonicity. Based on domain expertise, monotonicity plays a
vital role in numerous fairness-related areas, where violations could misguide
human decisions and lead to disastrous consequences. In this paper, we first
systematically evaluate the significance of applying monotonic neural additive
models (MNAMs), which use a fairness-aware ML algorithm to enforce both
individual and pairwise monotonicity principles, for the fairness of AI ethics
and society. We have found, through a hybrid method of theoretical reasoning,
simulation, and extensive empirical analysis, that considering monotonicity
axioms is essential in all areas of fairness, including criminology, education,
health care, and finance. Our research contributes to the interdisciplinary
research at the interface of AI ethics, explainable AI (XAI), and
human-computer interactions (HCIs). By evidencing the catastrophic consequences
if monotonicity is not met, we address the significance of monotonicity
requirements in AI applications. Furthermore, we demonstrate that MNAMs are an
effective fairness-aware ML approach by imposing monotonicity restrictions
integrating human intelligence.

应用神经附加模型，强制执行单调性原则以保证人工智能伦理和社会的公平性问题，是所有公平领域的必要因素，包括犯罪学，教育，医疗和金融等。

AI 伦理和社会的单调性：在犯罪学、教育、医疗保健和金融领域中单调神经加法模型的实证研究

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

A growing body of literature in fairness-aware ML (fairML) aspires to
mitigate machine learning (ML)-related unfairness in automated decision making
(ADM) by defining metrics that measure fairness of an ML model and by proposing
methods that ensure that trained ML models achieve low values in those
measures. However, the underlying concept of fairness, i.e., the question of
what fairness is, is rarely discussed, leaving a considerable gap between
centuries of philosophical discussion and recent adoption of the concept in the
ML community. In this work, we try to bridge this gap by formalizing a
consistent concept of fairness and by translating the philosophical
considerations into a formal framework for the training and evaluation of ML
models in ADM systems. We derive that fairness problems can already arise
without the presence of protected attributes, pointing out that fairness and
predictive performance are not irreconcilable counterparts, but rather that the
latter is necessary to achieve the former. Moreover, we argue why and how
causal considerations are necessary when assessing fairness in the presence of
protected attributes. We achieve greater linguistic clarity for the discussion
of fairML and propose general algorithms for practical applications.

本文旨在通过形式化一致的公平概念，将哲学讨论转化为 ADM 系统中 ML 模型培训和评估的一种正式框架，并提出了一般算法，以实现 fairML 的更高语言清晰度和实际应用。