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

arXiv: https://arxiv.org/abs/2409.11489 · HTML: https://mlsanity.com/paper/2409.11489/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Algorithmic Fairness from a Non-ideal Perspective](https://mlsanity.com/paper/2001.09773/) (2020)
- [Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance](https://mlsanity.com/paper/2206.11922/) (2022)
- [The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective](https://mlsanity.com/paper/2012.11448/) (2020)
- [The Impact of Data Preparation on the Fairness of Software Systems](https://mlsanity.com/paper/1910.02321/) (2019)
- [AI4OPT: AI Institute for Advances in Optimization](https://mlsanity.com/paper/2307.02671/) (2023)
- [Impact of Data Processing on Fairness in Supervised Learning](https://mlsanity.com/paper/2102.01867/) (2021)
- [AI Challenges for Society and Ethics](https://mlsanity.com/paper/2206.11068/) (2022)
- [Optimizing Transmission Infrastructure Investments to Support Line De-energization for Mitigating Wildfire Ignition Risk](https://mlsanity.com/paper/2203.10176/) (2022)
- [Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs](https://mlsanity.com/paper/2207.00241/) (2022)
- [A Framework for Risk Assessment and Optimal Line Upgrade Selection to Mitigate Wildfire Risk](https://mlsanity.com/paper/2110.07348/) (2021)
- [Equitably allocating wildfire resilience investments for power grids: The curse of aggregation and vulnerability indices](https://mlsanity.com/paper/2404.11520/) (2024)
- [Security Constrained Optimal Power Shutoff](https://mlsanity.com/paper/2304.13778/) (2023)

## Abstract (arXiv)

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.