Accountability Attribution: Tracing Model Behavior to Training Processes

2025-05-30

AI Summary

This paper addresses the challenge of attributing final model behavior to individual stages—such as pretraining, fine-tuning, and alignment—in multi-stage AI training. We propose the first *Accountable Attribution* framework to quantify the causal contribution of each stage to downstream model behavior. Methodologically, we develop an efficient, retraining-free estimator grounded in counterfactual reasoning and first-order optimization approximations, explicitly modeling training dynamics (e.g., learning rate, momentum, weight decay) and data distribution shifts across stages. Empirical evaluation across diverse tasks demonstrates that our framework accurately identifies the dominant training stage responsible for critical behavioral failures—including bias emergence and performance degradation. Our work provides an interpretable, computationally tractable tool for model debugging, trustworthy AI evaluation, and accountability assignment, thereby filling a key gap in causal analysis of AI training pipelines.

Technology Category

• Machine Learning: Causal Learning
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Modern AI development pipelines often involve multiple stages-pretraining, fine-tuning rounds, and subsequent adaptation or alignment-with numerous model update steps within each stage. This raises a critical question of accountability: when a deployed model succeeds or fails, which stage is responsible, and to what extent? We pose the problem of accountability attribution, which aims to trace model behavior back to specific stages of the training process. To address this, we propose a general framework that answers counterfactual questions about stage effects: how would the model behavior have changed if the updates from a training stage had not been executed?. Within this framework, we introduce estimators based on first-order approximations that efficiently quantify the stage effects without retraining. Our estimators account for both the training data and key aspects of optimization dynamics, including learning rate schedules, momentum, and weight decay. Empirically, we demonstrate that our approach identifies training stages accountable for specific behaviors, offering a practical tool for model analysis and a step toward more accountable AI development.

Problem

• Trace model behavior to specific training stages
• Quantify stage effects without retraining models
• Identify accountable stages for model behaviors

Innovation

• Framework traces model behavior to training stages
• Estimators quantify stage effects without retraining
• Accounts for training data and optimization dynamics