The burgeoning complexity of AI systems, particularly opaque models like Deep Neural Networks (DNNs), has underlined the importance of transparency and comprehensibility in decision-making processes. Specifically, as black-box models become widespread, stakeholders in AI are seeking insights into decision justifications that could be crucial in areas such as medicine and autonomous vehicles. As a result, transparency not only establishes ethical AI but also improves system performance by detecting biases, enhancing robustness against adversarial attacks, and ensuring an impactful impact from meaningful variables.
To address this critical need, Explainable AI (XAI) has been developed with the aim to balance model interpretability with high learning performance, fostering human understanding, trust, and effective management of AI interactions. XAI draws on social sciences and psychology to develop techniques to facilitate comprehension and transparency in the growing field of AI.
Several frameworks of XAI, including the What-If Tool (WIT), Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), DeepLIFT, ELI5, AI Explainability 360 (AIX360), Shapash, XAI library, OmniXAI1, and Activation atlases have successfully proven their effectiveness. These tools allow users to analyze machine learning systems, visualize model behavior, evaluate data feature importance, assess fairness metrics, and clarify ML predictions.
For instance, Google’s WIT helps users examine ML systems without extensive coding, enabling them to visualize model behavior and assess fairness. LIME reveals predictions of classifiers by learning an interpretable model around the prediction, ensuring comprehensibility and reliability. SHAP defines an importance value to each feature for a particular prediction, discovering a new category of additive feature importance measures. OmniXAI1, proposed by Salesforce researchers, offers a unified interface for understanding and interpreting ML decisions, assisting the generation of explanations, and visualizing insights, with minimal coding.
These tools aim to make ML more accessible and interpretable by providing clear, explicit labels and promoting better understanding and explanation of ML decision-making. They underline the indispensable need for accountability in AI and provide a platform for effective comprehensibility, fostering trust, accountability, and ethical AI implementation in different real-world applications. Consequently, the evolution of AI is increasingly steered by such XAI frameworks that ensure not only advanced performance but also ethical and understandable decision-making processes.
