Interpretability, Explainability, and Transparency
Summary
Interpretability, Explainability, and Transparency in AI encompass a range of approaches and techniques aimed at making complex machine learning models more understandable and accountable. These efforts include regularization for interpretability, neural-symbolic AI integration, neuron analysis, explanation methods, model reconstruction, inverse problem solving, neural network visualization, and the study of network modularity and local specialization. Researchers are developing methods to provide insights into model decision-making processes, such as LIME for explaining classifier predictions and techniques for visualizing neural network activations. The field also explores the use of sparse linear models and investigates human understanding of interpretable models. These approaches aim to address challenges in algorithmic fairness, bias identification, and model verification while balancing the need for transparency with concerns about model confidentiality. As AI systems become more prevalent in critical applications, the development of interpretable and explainable AI remains crucial for ensuring trust, safety, and alignment with human values.