Explanation Methods
Summary
Explanation methods in AI aim to provide insights into the decision-making processes of complex machine learning models, addressing the need for transparency and interpretability. These methods are crucial for ensuring algorithmic fairness, identifying potential biases, and verifying that algorithms perform as expected. Techniques like LIME (Local Interpretable Model-agnostic Explanations) have been developed to explain predictions of any classifier in an interpretable and faithful manner. However, the field of explainable AI (XAI) faces challenges in standardization and systematic assessment of explanations. To effectively deploy explainable machine learning, it is essential to consider the needs of various stakeholders, including end-users, regulators, and domain experts. Current approaches, especially for deep neural networks, are often insufficient, highlighting the need for further research and development of best practices in explanatory artificial intelligence.