ABSTRACT
This chapter discusses the importance of AI interpretability in various domains, highlighting its need for transparency and explainability. It categorizes AI interpretability strategies into model-based, post-hoc explanation, and hybrid methods. Model-based methods, such as Decision Trees, Linear Models, Feature Selection, Regularization, and Sparse Models, are transparent but may compromise predictive accuracy. Hybrid techniques combine model-based and post-hoc methods for accuracy and interpretability. However, these methods also face drawbacks such as performance trade-offs, scalability and complexity concerns, human variables, cognitive biases, and lack of clear legal frameworks. The report emphasizes the need for balancing interpretability and performance with social, ethical, and legal ramifications. Future avenues for AI interpretability include machine learning improvements, interdisciplinary partnerships, standards and norms, and ethical and social research. Real-world applications require explainable deep learning models and ethical norms. The study emphasizes the need for advancement, cooperation, and regulatory frameworks to ensure trustworthy and ethical AI system usage. It also highlights the need for explainable deep learning models and ethical norms for real-world applications.
