The rapid advancement of artificial intelligence has led to increasingly sophisticated deep learning models, which frequently operate as opaque 'black boxes' with limited transparency in their decision-making processes. This lack of interpretability presents considerable challenges, especially in high-stakes applications where understanding the rationale behind a model's outputs is as essential as the outputs themselves. This study addresses the pressing need for interpretability in AI systems, emphasizing its role in fostering trust, ensuring accountability, and promoting responsible deployment in mission-critical fields. To address the interpretability challenge in deep learning, we introduce DLBacktrace, an innovative technique developed by the AryaXAI team to illuminate model decisions across a wide array of domains, including simple Multi Layer Perceptron (MLPs), Convolutional Neural Networks (CNNs), Large Language Models (LLMs), Computer Vision Models, and more. We provide a comprehensive overview of the DLBacktrace algorithm and present benchmarking results, comparing its performance against established interpretability methods, such as SHAP, LIME, GradCAM, Integrated Gradients, SmoothGrad, and Attention Rollout, using diverse task-based metrics. The proposed DLBacktrace technique is compatible with various model architectures built in PyTorch and TensorFlow, supporting models like Llama 3.2, other NLP architectures such as BERT and LSTMs, computer vision models like ResNet and U-Net, as well as custom deep neural network (DNN) models for tabular data. This flexibility underscores DLBacktrace's adaptability and effectiveness in enhancing model transparency across a broad spectrum of applications. The library is open-sourced and available at https://github.com/AryaXAI/DLBacktrace .

翻译：人工智能的快速发展催生了日益复杂的深度学习模型，这些模型通常作为不透明的“黑箱”运行，其决策过程缺乏透明度。这种可解释性的缺失带来了巨大挑战，尤其是在高风险应用中，理解模型输出背后的逻辑与输出本身同等重要。本研究针对人工智能系统对可解释性的迫切需求，强调其在关键任务领域中建立信任、确保问责制和促进负责任部署方面的重要作用。为解决深度学习中的可解释性挑战，我们提出了由AryaXAI团队开发的创新技术DLBacktrace，该技术能够阐明跨广泛领域的模型决策，包括简单的多层感知机（MLP）、卷积神经网络（CNN）、大语言模型（LLM）、计算机视觉模型等。我们全面概述了DLBacktrace算法，并展示了基准测试结果，使用多样化的基于任务的指标，将其性能与SHAP、LIME、GradCAM、Integrated Gradients、SmoothGrad和Attention Rollout等成熟的可解释性方法进行了比较。所提出的DLBacktrace技术与基于PyTorch和TensorFlow构建的各种模型架构兼容，支持诸如Llama 3.2、其他NLP架构（如BERT和LSTM）、计算机视觉模型（如ResNet和U-Net），以及用于表格数据的定制深度神经网络（DNN）模型。这种灵活性凸显了DLBacktrace在广泛的应用场景中增强模型透明度的适应性和有效性。该库已开源，可通过https://github.com/AryaXAI/DLBacktrace 获取。