Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
arXiv:2607.08641v1 Announce Type: new Abstract: Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes into developing techniques for interpreting the interactions learned by a given model, fewer studies focus on assessing the quality of such explanations. Even fewer focus on how to adjust the model to produce explanations faithful to prior knowledge, a process known as explanation-guided learning. Furthermore, most approaches in this area focus on classification problems and usually assume prior
The increased focus on AI interpretability and explanation-guided learning, particularly beyond classification problems, is driving renewed interest in methodologies that make AI more trustworthy and controllable.
This research contributes to the fundamental shift towards more interpretable and controllable AI, moving beyond opaque 'black box' models to systems that can incorporate prior knowledge and provide faithful explanations.
The ability to 'steer' neural network training based on interpretable constraints allows for the development of AI models that are not only high-performing but also align with human understanding and expectations.
- · AI ethicists and regulatory compliance platforms
- · Developers of safety-critical AI systems
- · Industries requiring high-trust AI (e.g., healthcare, finance)
- · Researchers in explainable AI (XAI)
- · Developers focused solely on model accuracy
- · AI systems with opaque decision-making processes
- · Companies unable to explain their AI's behavior
More interpretable and trustworthy AI models will become standard, especially in regulated industries.
This will accelerate the adoption of AI in sectors where transparency and accountability are paramount, potentially leading to new regulatory frameworks.
The ability to 'steer' AI could enable more sophisticated human-AI collaboration and agentic systems that adhere to complex, human-defined constraints.
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Read at arXiv cs.LG