Explainable AI (XAI) is about designing AI systems to be transparent and understandable to humans. It’s the idea that people should be able to comprehend why an AI made a particular decision, making AI more accountable and reliable.
Imagine your robotic toy makes decisions on its own – like picking up a blue block instead of a red one. It wouldn’t be great if you have no clue why it did that, right? Explainable AI is like your toy telling you why it wanted the blue block, maybe because it matches its own color.
Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence research where the results of the solution can be understood by humans. As AI models become more complex, their decisions often become more opaque - this is often the case with more complex deep learning models, known as the ‘black box’ problem. XAI aims to tackle this black box problem by making the AI model’s decisions interpretable and understandable.
While this sounds straightforward, achieving interpretability in AI systems is complex. It involves both technical methods (like model-specific or model-agnostic interpretability methods) and non-technical considerations (like data privacy and user trust).
Technical strategies for creating explainable models often involve a trade-off between performance and interpretability. For example, simpler models such as linear regression or decision trees are inherently more interpretable but may not perform as well on complex tasks as uninterpretable but higher-performing models, such as neural networks.
Model-agnostic methods offer another route to XAI. They give explanations independent of the specific type of model used. These include methods such as LIME (Local Interpretable Model-Agnostic Explanations), which explains predictions of any classifier by approximating it locally with an interpretable model, and SHAP (SHapley Additive exPlanations), which interprets the output of any machine learning model but does so using game theory to attribute the contribution of each feature to the prediction.
Non-technical considerations are about ensuring these explanations are truly useful for end-users which may vary depending on audience: what a data scientist might find helpful is different from a doctor or a judge. Moreover, the explanations must not reveal sensitive data, bringing in the importance of data privacy into XAI.
Ultimately, Explainable AI aims to create more transparency, accountability, and fairness in AI systems, by offering humans clear, understandable reasons for AI decisions. It brings together techniques from AI, machine learning, user experience design, psychology, law, and numerous other fields to achieve this.
Interpretable Machine Learning (ML),, Black Box Model, LIME, SHAP, Fairness in AI, AI Accountability, AI Transparency, Feature Importance, Model-agnostic methods, Decision Tree, Linear Regression, Neural Networks, AI Ethics