ML Compass: Navigating Capability, Cost, and Compliance Trade-offs in AI Model Deployment

2025-12-29

AI Summary

Existing AI model leaderboards inadequately support real-world deployment decisions due to their inability to jointly optimize capability, cost, and regulatory compliance. Method: We propose a system-level constrained optimization framework that formally defines the “capability–cost frontier” and reveals its tripartite optimal structure; develop an interpretable comparative statics analysis to quantify how budgetary, regulatory, and technological shifts affect multi-objective trade-offs; and integrate low-dimensional internal metric extraction, empirical frontier estimation, task-driven utility learning, and constraint-aware recommendation into a unified pipeline. Contribution/Results: The framework enables deployment-value assessment across domains (e.g., dialogue and healthcare). Empirical evaluation on PRISM and HealthBench yields “deployment-aware leaderboards” whose rankings substantially diverge from pure capability-based rankings—enhancing decision rationality and regulatory robustness.

Technology Category

• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

We study how organizations should select among competing AI models when user utility, deployment costs, and compliance requirements jointly matter. Widely used capability leaderboards do not translate directly into deployment decisions, creating a capability--deployment gap; to bridge it, we take a systems-level view in which model choice is tied to application outcomes, operating constraints, and a capability--cost frontier. We develop ML Compass, a framework that treats model selection as constrained optimization over this frontier. On the theory side, we characterize optimal model configurations under a parametric frontier and show a three-regime structure in optimal internal measures: some dimensions are pinned at compliance minima, some saturate at maximum levels, and the remainder take interior values governed by frontier curvature. We derive comparative statics that quantify how budget changes, regulatory tightening, and technological progress propagate across capability dimensions and costs. On the implementation side, we propose a pipeline that (i) extracts low-dimensional internal measures from heterogeneous model descriptors, (ii) estimates an empirical frontier from capability and cost data, (iii) learns a user- or task-specific utility function from interaction outcome data, and (iv) uses these components to target capability--cost profiles and recommend models. We validate ML Compass with two case studies: a general-purpose conversational setting using the PRISM Alignment dataset and a healthcare setting using a custom dataset we build using HealthBench. In both environments, our framework produces recommendations -- and deployment-aware leaderboards based on predicted deployment value under constraints -- that can differ materially from capability-only rankings, and clarifies how trade-offs between capability, cost, and safety shape optimal model choice.

Problem

• Selecting AI models considering utility, cost, and compliance trade-offs
• Bridging the gap between capability rankings and deployment decisions
• Optimizing model choice under constraints via a systems-level framework

Innovation

• Framework for constrained optimization model selection
• Pipeline extracts measures, estimates frontier, learns utility
• Generates deployment-aware rankings beyond capability leaderboards