Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools
This work addresses ethical deployment issues for researchers and practitioners in AI and optimization, but it is incremental as it builds on existing fairness discussions without introducing new methods or data.
The paper tackles the challenge of ethically deploying AI-enabled optimization in engineered systems like power grids and supply chains, highlighting the need to move beyond algorithmic fairness to address ethical considerations across modeling, data, analysis, and implementation stages. It uses case studies to foster reflection among researchers rather than providing prescriptive rules.
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.