Algorithm fairness has become a central problem for the broad adoption of
artificial intelligence. Although the past decade has witnessed an explosion of
excellent work studying algorithm biases, achieving fairness in real-world AI
production systems has remained a challenging task. Most existing works fail to
excel in practical applications since either they have conflicting measurement
techniques and/ or heavy assumptions, or require code-access of the production
models, whereas real systems demand an easy-to-implement measurement framework
and a systematic way to correct the detected sources of bias.
In this paper, we leverage recent advances in causal inference and
interpretable machine learning to present an algorithm-agnostic framework
(MIIF) to Measure, Interpret, and Improve the Fairness of an algorithmic
decision. We measure the algorithm bias using randomized experiments, which
enables the simultaneous measurement of disparate treatment, disparate impact,
and economic value. Furthermore, using modern interpretability techniques, we
develop an explainable machine learning model which accurately interprets and
distills the beliefs of a blackbox algorithm. Altogether, these techniques
create a simple and powerful toolset for studying algorithm fairness,
especially for understanding the cost of fairness in practical applications
like e-commerce and targeted advertising, where industry A/B testing is already
abundant.

本文利用因果推断和解释性机器学习的最新进展，提出了一种算法无关的框架 (MIIF)，用于测量、解释和改进算法决策的公平性。我们使用随机实验来测量算法偏差，从而能够同时测量不同对待、不同影响和经济价值。此外，利用现代解释性技术，我们开发了一个可解释的机器学习模型，准确解释和提炼黑盒算法的信念。总的来说，这些技术为研究算法公平性创造了一个简单而强大的工具集，特别适用于理解实际应用中公平性成本的电子商务和目标广告等领域，其中行业的 A/B 测试已经非常丰富。

利用因果推断和随机实验测量、解释和改善算法的公平性

Measuring, Interpreting, and Improving Fairness of Algorithms using  Causal Inference and Randomized Experiments

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。