标题： 问责制归因：追溯模型行为到训练过程 显示英文标题

标题： Accountability Attribution: Tracing Model Behavior to Training Processes

摘要： 现代人工智能开发流程通常涉及多个阶段——预训练、微调轮次，以及后续的适应或对齐，并且在每个阶段中有大量的模型更新步骤。 这就引发了一个关键的问题：问责制。当部署的模型成功或失败时，哪个阶段应对此负责，以及在多大程度上？ 我们提出了一个问责制归因问题，旨在将模型行为追溯到特定的训练过程阶段。 为了解决这个问题，我们提出了一种通用框架，用于回答关于阶段效果的反事实问题：如果某个训练阶段的更新未被执行，模型的行为会如何变化？ 在这个框架内，我们引入了基于一阶近似的方法，这些方法可以高效地量化阶段效果而无需重新训练。 我们的估计器考虑了训练数据和优化动态的关键方面，包括学习率计划、动量和权重衰减。 实证研究表明，我们的方法能够识别导致特定行为的训练阶段，为模型分析提供了一个实用工具，并朝着更负责任的人工智能开发迈出了一步。 显示英文摘要

摘要： 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.