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.
翻译:应用人工智能(AI)的公平性是更好的社会所必不可少的。作为社会机制的基本轴心,公平性由多个方面组成。虽然机器学习(ML)社区把焦点放在交叉性上,将其作为一个统计均等问题,特别是在歧视问题上,但新兴的文献机构处理另一个方面 -- -- 单一论。根据域内的专门知识,单一论在许多与公平相关的领域发挥着关键作用,在这些领域中,侵权可能误导人类决策并导致灾难性后果。在本文件中,我们首先系统地评估应用单一神经添加模型(MNAMs)的重要性,该模型使用公平觉悟的ML算法来执行个人和对等的单一性原则,以维护AI道德和社会的公平性。我们发现,通过理论推理、模拟和广泛的实证分析的混合方法,考虑单一感性轴心在所有公平领域都至关重要,包括犯罪学、教育、保健和金融等领域。我们的研究有助于在AI伦理的界面进行跨学科研究,解释AI(XAI)的公平性计算,而单体间应用则是将单体-机互动(MICHI-HI-ICx)的比照,如果我们能够解释,那么,那么,那么一体-CIHIHI-C-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I