risks mitigation

Interpreting deep learning models is crucial for diverse applications such as healthcare and self-driving cars. Understandably, errors can have catastrophic consequences. Thus, achieving interpretability is essential for decision-makers. Properties like fidelity, comprehensibility, and accuracy are vital for evaluating interpretability. Various methods, including visualization techniques and knowledge distillation, offer insights into complex models. However, quantifying interpretability remains a challenge. For more information, refer to research papers on refining deep neural networks and interpreting CNNs. Enhancing interpretability not only fosters trust in AI but also mitigates risks in decision-making processes.