arXiv:2605.19848v2 Announce Type: replace Abstract: In recent years, the black-box nature of deep learning models has limited their application in high-stakes domains such as medical diagnosis and finance, where interpretability is essential. To address this, we propose a novel approach using influence functions to enhance interpretability in NLP models at both the sample and concept levels. Experiments on CEBaB and Yelp datasets show that influence functions effectively identify the most impactful training samples, both helpful and harmful, on model predictions. By adjusting the labels and we
The increasing complexity and black-box nature of deep learning models, especially in high-stakes applications, are driving urgent demand for greater interpretability and transparency now.
This development improves trust and adoption of AI in critical sectors by enabling better understanding, debugging, and regulatory compliance for complex models, addressing a significant barrier to wider deployment.
The ability to identify influential training samples at a concept level provides a new mechanism for ensuring model fairness, robustness, and explainability beyond just sample-level analysis.
- · AI developers
- · Healthcare sector
- · Financial sector
- · Regulatory bodies
- · Opaque AI vendors
- · Pure 'black-box' model approaches
Increased interpretability leads to more reliable and auditable AI systems being deployed in sensitive applications.
New regulatory frameworks may emerge, mandating concept-level transparency for AI systems, especially in areas like bias detection.
The commoditization of interpretable AI tools could accelerate the adoption of AI in smaller, risk-averse organizations, broadly expanding the AI market.
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