Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools

TL;DR

This paper emphasizes the importance of ethical considerations beyond fairness in AI-enabled optimization, advocating for systematic ethical decision-making throughout the development and deployment of decision-support tools in engineered systems.

Contribution

It introduces a framework for ethical decision-making in AI-optimization, supported by case studies, and encourages ongoing reflection rather than prescriptive rules.

Findings

Identifies ethical challenges in AI-optimization in power and logistics systems.

Highlights the need for ethical awareness at all stages of model development.

Provides case studies illustrating ethical considerations in practice.

Abstract

The integration of artificial intelligence (AI) and optimization hold substantial promise for improving the efficiency, reliability, and resilience of engineered systems. Due to the networked nature of many engineered systems, ethically deploying methodologies at this intersection poses challenges that are distinct from other AI settings, thus motivating the development of ethical guidelines tailored to AI-enabled optimization. This paper highlights the need to go beyond fairness-driven algorithms to systematically address ethical decisions spanning the stages of modeling, data curation, results analysis, and implementation of optimization-based decision support tools. Accordingly, this paper identifies ethical considerations required when deploying algorithms at the intersection of AI and optimization via case studies in power systems as well as supply chain and logistics. Rather than providing a prescriptive set of rules, this paper aims to foster reflection and awareness among researchers and encourage consideration of ethical implications at every step of the decision-making process.