# Deontological Ethics By Monotonicity Shape Constraints

arXiv: https://arxiv.org/abs/2001.11990 · HTML: https://mlsanity.com/paper/2001.11990/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 16 of 16)

- [Explanations for Monotonic Classifiers](https://mlsanity.com/paper/2106.00154/) (2021)
- ["There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making](https://mlsanity.com/paper/2205.05758/) (2022)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)
- [Aligning Model Properties via Conformal Risk Control](https://mlsanity.com/paper/2406.18777/) (2024)
- [Smooth Min-Max Monotonic Networks](https://mlsanity.com/paper/2306.01147/) (2023)
- [A Ranking Approach to Fair Classification](https://mlsanity.com/paper/2102.04565/) (2021)
- [Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance](https://mlsanity.com/paper/2301.07060/) (2023)
- [MonoKAN: Certified Monotonic Kolmogorov-Arnold Network](https://mlsanity.com/paper/2409.11078/) (2024)
- [Global Optimization Networks](https://mlsanity.com/paper/2202.01277/) (2022)
- [Demographic parity in regression and classification within the unawareness framework](https://mlsanity.com/paper/2409.02471/) (2024)
- [A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making](https://mlsanity.com/paper/2205.00033/) (2022)
- [A mathematical certification for positivity conditions in Neural Networks with applications to partial monotonicity and Trustworthy AI](https://mlsanity.com/paper/2406.08525/) (2024)
- [On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study](https://mlsanity.com/paper/2509.04053/) (2025)
- [Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias](https://mlsanity.com/paper/2606.17886/) (2026)
- [Hidden Monotonicity: Explaining Deep Neural Networks via their DC Decomposition](https://mlsanity.com/paper/2601.07700/) (2026)
- [The Cost of a Physics Prior Is Bounded by the Ablation Gap](https://mlsanity.com/paper/2608.21059/) (2026)

## Abstract (arXiv)

We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding shape constraints that constrain the model to respond only positively to relevant inputs. We analyze the relationship between these deontological constraints that act on individuals and the consequentialist group-based fairness goals of one-sided statistical parity and equal opportunity. This strategy works with sensitive attributes that are Boolean or real-valued such as income and age, and can help produce more responsible and trustworthy AI.