AI Model Explainability refers to the ability to understand and interpret how an AI model arrives at its predictions or decisions. In other words, it's about making the inner workings and reasoning of AI systems transparent and comprehensible to humans.
Explainability is crucial because as AI models become more complex and are increasingly used in high-stakes domains like healthcare, finance, and legal systems, it's essential to ensure they are making decisions fairly, ethically, and without unintended biases. Black box models that provide outputs without any insight into how those outputs were determined can be problematic. If an AI system denies someone a loan, makes a medical diagnosis, or recommends a prison sentence, there needs to be a way to understand the factors that influenced that outcome. Explainability enables users to trust the model, verify it's working as intended, and identify potential flaws or biases.
Additionally, in many industries there are regulatory requirements around explainability. For example, the EU's GDPR legislation includes a "right to explanation" that entitles individuals to explanations of algorithmic decisions that significantly affect them. As AI becomes more prevalent, the ability to explain and justify model outcomes to stakeholders like end users, regulators, and society at large will only grow in importance. Techniques to enhance explainability include using inherently interpretable models when possible, generating explanations of black box model outputs (e.g. LIME, SHAP), and improving transparency around AI systems' development and deployment. Making progress in AI explainability will be key to unlocking AI's full potential while ensuring it remains ethical and accountable.