Deontological Ethics By Monotonicity Shape Constraints

TL;DR

This paper introduces a method to embed deontological ethical principles into machine learning models using shape constraints, aiming to enhance AI responsibility and trustworthiness.

Contribution

It proposes a novel approach of incorporating ethical constraints via shape constraints in models, linking deontological principles with fairness goals.

Findings

Shape constraints can enforce ethical principles in models.

The approach applies to sensitive attributes like income and age.

It promotes more responsible AI behavior.

Abstract

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.