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Domain-Agnostic Scalable AI Safety Ensuring Framework

Beomjun Kim, Kangyeon Kim, Sunwoo Kim, Yeonsang Shin, Heejin Ahn

TL;DR

The paper tackles the challenge of enforcing safety across arbitrary AI applications by reframing safety as probabilistic constraint satisfaction and introducing a domain-agnostic framework that integrates an optimization layer with chance constraints, a safety classification model, internal test data, and conservative testing. A key contribution is the theoretical development of a scaling law linking the quantity of internal test data to the safety-performance trade-off, supported by a formal convergence result for the proposed approximate loss. Empirically, the framework demonstrates unprecedented safety performance across multiple domains—reinforcement learning (SafetyGym), natural language generation, and production planning—while maintaining competitive performance, as evidenced by a dramatic reduction in collisions in RL and improvement in constraint satisfaction in NLP. The work provides a principled, generalizable foundation for deploying AI in safety-critical settings, with rigorous guarantees, documented training and evaluation procedures, and practical deployment strategies such as bias-correction for threshold tuning.

Abstract

AI safety has emerged as a critical priority as these systems are increasingly deployed in real-world applications. We propose the first domain-agnostic AI safety ensuring framework that achieves strong safety guarantees while preserving high performance, grounded in rigorous theoretical foundations. Our framework includes: (1) an optimization component with chance constraints, (2) a safety classification model, (3) internal test data, (4) conservative testing procedures, (5) informative dataset quality measures, and (6) continuous approximate loss functions with gradient computation. Furthermore, to our knowledge, we mathematically establish the first scaling law in AI safety research, relating data quantity to safety-performance trade-offs. Experiments across reinforcement learning, natural language generation, and production planning validate our framework and demonstrate superior performance. Notably, in reinforcement learning, we achieve 3 collisions during 10M actions, compared with 1,000-3,000 for PPO-Lag baselines at equivalent performance levels -- a safety level unattainable by previous AI methods. We believe our framework opens a new foundation for safe AI deployment across safety-critical domains.

Domain-Agnostic Scalable AI Safety Ensuring Framework

TL;DR

The paper tackles the challenge of enforcing safety across arbitrary AI applications by reframing safety as probabilistic constraint satisfaction and introducing a domain-agnostic framework that integrates an optimization layer with chance constraints, a safety classification model, internal test data, and conservative testing. A key contribution is the theoretical development of a scaling law linking the quantity of internal test data to the safety-performance trade-off, supported by a formal convergence result for the proposed approximate loss. Empirically, the framework demonstrates unprecedented safety performance across multiple domains—reinforcement learning (SafetyGym), natural language generation, and production planning—while maintaining competitive performance, as evidenced by a dramatic reduction in collisions in RL and improvement in constraint satisfaction in NLP. The work provides a principled, generalizable foundation for deploying AI in safety-critical settings, with rigorous guarantees, documented training and evaluation procedures, and practical deployment strategies such as bias-correction for threshold tuning.

Abstract

AI safety has emerged as a critical priority as these systems are increasingly deployed in real-world applications. We propose the first domain-agnostic AI safety ensuring framework that achieves strong safety guarantees while preserving high performance, grounded in rigorous theoretical foundations. Our framework includes: (1) an optimization component with chance constraints, (2) a safety classification model, (3) internal test data, (4) conservative testing procedures, (5) informative dataset quality measures, and (6) continuous approximate loss functions with gradient computation. Furthermore, to our knowledge, we mathematically establish the first scaling law in AI safety research, relating data quantity to safety-performance trade-offs. Experiments across reinforcement learning, natural language generation, and production planning validate our framework and demonstrate superior performance. Notably, in reinforcement learning, we achieve 3 collisions during 10M actions, compared with 1,000-3,000 for PPO-Lag baselines at equivalent performance levels -- a safety level unattainable by previous AI methods. We believe our framework opens a new foundation for safe AI deployment across safety-critical domains.
Paper Structure (95 sections, 12 theorems, 159 equations, 8 figures, 9 tables)

This paper contains 95 sections, 12 theorems, 159 equations, 8 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Domain-Agnostic AI Safety Framework
  4. Problem Setup & Notations
  5. Our Framework: Dealing with Safety Constraints
  6. Optimization Component
  7. Chance Constraints
  8. Safety Classification Model
  9. Internal Test Data
  10. $\zeta$-informative
  11. Conservative Testing
  12. Training & Running the Framework
  13. Approximate Loss Function
  14. Gradient Computation
  15. Bias Correction when Running
  16. ...and 80 more sections

Key Result

Proposition 1

When both $\mathcal{S}_{cr}$ and $\mathcal{O}_{cr}$ are finite, our compressed constraint replacement (Equation rep_const) based on modified chance-constrained formulation (Equation ccon_def) is included into our general optimization problem (Equation gen_optlayer). In addition, the whole procedure,

Figures (8)

  • Figure 1:
  • Figure 2: Reinforcement learning. (A) Our framework achieves dramatically low collision rates with competitive performance. (B) Our framework demonstrates clear scaling properties.
  • Figure 3: Natural language generation. (A) Our framework outperforms our baseline and ablations (rejection sampling). (B) Our framework successfully achieves constraint violations lower than designated thresholds. (C) Our framework demonstrates clear scaling properties.
  • Figure 4: Conceptual illustration for Theorem \ref{['property_loss']}.
  • Figure 5: System structure based on the proposed framework for demand prediction-based production decision.
  • ...and 3 more figures

Theorems & Definitions (25)

  • Proposition 1
  • proof
  • Definition 1
  • Proposition 2
  • proof
  • Theorem 1
  • proof
  • Corollary 1
  • Corollary 2
  • Definition 2
  • ...and 15 more