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

Rosemarie Santa Gonzalez, Ryan Piansky, Sue M Bae, Justin Biddle, Daniel Molzahn

TL;DR

This paper argues that ethical deployment of AI-enabled optimization in networked engineered systems requires guidelines beyond algorithmic fairness, spanning data collection, modeling, results interpretation, and implementation. Through case studies in collaborative decentralized cold supply chains and power systems under wildfire risk, it demonstrates how fairness, transparency, accountability, and inclusivity must be integrated throughout the optimization pipeline. It contributes a set of lessons and guidelines that emphasize human-in-the-loop decision-making, stakeholder engagement, and continuous monitoring to align optimization with broader societal values. The work highlights practical implications for equitable infrastructure planning and community-focused resource allocation, aiming to foster responsible and trustworthy AI-enabled decision-support tools across sectors.

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.

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

TL;DR

This paper argues that ethical deployment of AI-enabled optimization in networked engineered systems requires guidelines beyond algorithmic fairness, spanning data collection, modeling, results interpretation, and implementation. Through case studies in collaborative decentralized cold supply chains and power systems under wildfire risk, it demonstrates how fairness, transparency, accountability, and inclusivity must be integrated throughout the optimization pipeline. It contributes a set of lessons and guidelines that emphasize human-in-the-loop decision-making, stakeholder engagement, and continuous monitoring to align optimization with broader societal values. The work highlights practical implications for equitable infrastructure planning and community-focused resource allocation, aiming to foster responsible and trustworthy AI-enabled decision-support tools across sectors.

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.
Paper Structure (37 sections, 2 equations, 2 figures, 1 table)

This paper contains 37 sections, 2 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Ethical Considerations in AI-Enabled Optimization
  3. Optimization Models
  4. Optimization Process
  5. Case Studies in AI-Enabled Optimization
  6. Case Study 1: Ethical Challenges in Collaborative Decentralized Cold Food Supply Chains
  7. Ethical Considerations in Collaborative Decentralized Cold Supply Chains
  8. Data
  9. Modeling
  10. Results
  11. Implications and Recommendations
  12. Case Study 2: Ethical Challenges in Power Systems’ Transmission Infrastructure
  13. Ethical Considerations in Transmission Infrastructure
  14. Data
  15. Modeling
  16. ...and 22 more sections

Figures (2)

  • Figure 1: Four Stages of an Optimization Pipeline, from Data Collection, Modeling, Results, and Implications and Recommendations.
  • Figure 2: Summary of Ethical Challenges Identified Through Case Studies.