mlsanityData to 14 Oct 2026

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

arXiv 2301.07060 ↗First posted 17 Jan 2023cs.LG cs.AI cs.CYcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 16 of its 52 references that are in the map. First posted on arXiv 17 Jan 2023.

Builds on

16 of 52 references.

References · 16

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2014Certifying and removing disparate impactcited by 715
  3. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 319
  4. 2018An Intersectional Definition of Fairnesscited by 127
  5. 2020GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactionscited by 82
  6. 2019Machine Learning Methods Economists Should Know Aboutcited by 64
  7. 2017Deep Lattice Networks and Partial Monotonic Functionscited by 63
  8. 2017Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillationcited by 62
  9. 2020Certified Monotonic Neural Networkscited by 51
  10. 2021The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systemscited by 39
  11. 2020Counterexample-Guided Learning of Monotonic Neural Networkscited by 34
  12. 2018An Interpretable Model with Globally Consistent Explanations for Credit Riskcited by 33
4 more
  1. 2019Enhancing Explainability of Neural Networks through Architecture Constraintscited by 29
  2. 2020How is Machine Learning Useful for Macroeconomic Forecasting?cited by 18
  3. 2020Deontological Ethics By Monotonicity Shape Constraintscited by 16
  4. 2022Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoringcited by 12

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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2023FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicitycited by 25
  2. 2023Fairness of ChatGPTcited by 20
  3. 2024A mathematical certification for positivity conditions in Neural Networks with applications to partial monotonicity and Trustworthy AIcited by 1
  4. 2024Fine-Grained Detoxification via Instance-Level Prefixes for Large Language Modelscited by 0