# Optimization's Neglected Normative Commitments

arXiv: https://arxiv.org/abs/2305.17465 · HTML: https://mlsanity.com/paper/2305.17465/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 133 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 27 of 27)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Reconciling modern machine learning practice and the bias-variance trade-off](https://mlsanity.com/paper/1812.11118/) (2018)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty](https://mlsanity.com/paper/2011.07586/) (2020)
- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [Post-hoc Uncertainty Calibration for Domain Drift Scenarios](https://mlsanity.com/paper/2012.10988/) (2020)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research](https://mlsanity.com/paper/2102.01203/) (2021)
- [Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness](https://mlsanity.com/paper/2010.05434/) (2020)
- [POTs: Protective Optimization Technologies](https://mlsanity.com/paper/1806.02711/) (2018)
- [Hard Choices in Artificial Intelligence](https://mlsanity.com/paper/2106.11022/) (2021)
- [From Optimizing Engagement to Measuring Value](https://mlsanity.com/paper/2008.12623/) (2020)
- [Reward Reports for Reinforcement Learning](https://mlsanity.com/paper/2204.10817/) (2022)
- [Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering](https://mlsanity.com/paper/1910.04223/) (2019)
- [Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems](https://mlsanity.com/paper/2011.14890/) (2020)
- [The Objective Function: Science and Society in the Age of Machine Intelligence](https://mlsanity.com/paper/2209.10418/) (2022)
- [Optimization of Battery Energy Storage to Improve Power System Oscillation Damping](https://mlsanity.com/paper/1811.10213/) (2018)
- [Correctness of Speculative Optimizations with Dynamic Deoptimization](https://mlsanity.com/paper/1711.03050/) (2017)
- [Compounding Injustice: History and Prediction in Carceral Decision-Making](https://mlsanity.com/paper/2005.13404/) (2020)

## Built on by (most cited first, 4 of 4)

- [What Constitutes a Less Discriminatory Algorithm?](https://mlsanity.com/paper/2412.18138/) (2024)
- [Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making](https://mlsanity.com/paper/2502.08542/) (2025)
- [Rethinking Optimization: A Systems-Based Approach to Social Externalities](https://mlsanity.com/paper/2506.12825/) (2025)
- [Consumer Autonomy or Illusion? Rethinking Consumer Agency in the Age of Algorithms](https://mlsanity.com/paper/2508.13440/) (2025)

## Abstract (arXiv)

Optimization is offered as an objective approach to resolving complex, real-world decisions involving uncertainty and conflicting interests. It drives business strategies as well as public policies and, increasingly, lies at the heart of sophisticated machine learning systems. A paradigm used to approach potentially high-stakes decisions, optimization relies on abstracting the real world to a set of decision(s), objective(s) and constraint(s). Drawing from the modeling process and a range of actual cases, this paper describes the normative choices and assumptions that are necessarily part of using optimization. It then identifies six emergent problems that may be neglected: 1) Misspecified values can yield optimizations that omit certain imperatives altogether or incorporate them incorrectly as a constraint or as part of the objective, 2) Problematic decision boundaries can lead to faulty modularity assumptions and feedback loops, 3) Failing to account for multiple agents' divergent goals and decisions can lead to policies that serve only certain narrow interests, 4) Mislabeling and mismeasurement can introduce bias and imprecision, 5) Faulty use of relaxation and approximation methods, unaccompanied by formal characterizations and guarantees, can severely impede applicability, and 6) Treating optimization as a justification for action, without specifying the necessary contextual information, can lead to ethically dubious or faulty decisions. Suggestions are given to further understand and curb the harms that can arise when optimization is used wrongfully.