Optimization's Neglected Normative Commitments
arXiv:2305.17465 · doi:10.1145/3593013.3593976
Abstract
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.
arXiv comment14 pages, 1 figure, presentation at FAccT23
References in corpus (14)
- Model Cards for Model Reporting
- Reconciling modern machine learning practice and the bias-variance trade-off
- Theoretically Principled Trade-off between Robustness and Accuracy
- Cooperative Inverse Reinforcement Learning
- Measurement and Fairness
- Runaway Feedback Loops in Predictive Policing
- Optimization of Battery Energy Storage to Improve Power System Oscillation Damping
- Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
- POTs: Protective Optimization Technologies
- From Optimizing Engagement to Measuring Value
- Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
- Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification
- The Objective Function: Science and Society in the Age of Machine Intelligence
- Compounding Injustice: History and Prediction in Carceral Decision-Making