XAI (Explainable AI) techniques aim to make AI models more transparent by providing explanations of their decision-making processes. These techniques can be categorized by when the explanation is provided (before or after model training) and the scope of the explanation (global or local). Post-hoc methods analyze models after training, while ante-hoc methods involve inherently explainable models, like decision trees. For example, decision trees can directly explain why an employee is at risk of leaving based on factors like tenure and workload, while neural networks might require post-hoc techniques like SHAP or LIME to explain predictions, such as the risk of a disease.
XAI explanations are also divided into global and local categories. Global explanations provide insights into a model’s behavior across all cases, such as understanding how a bank’s loan model weighs factors like credit scores in its decisions. This helps ensure fairness and regulatory compliance. Local explanations, on the other hand, focus on specific predictions, like a healthcare model’s reasoning for diagnosing a particular patient, helping medical professionals trust and personalize care.
In addition to global and local explanations, XAI techniques are tailored to different use cases and stakeholder needs. They can assist developers in debugging, improve model accuracy, detect biases, or support product teams in personalizing services. Ongoing academic research is expanding the capabilities of XAI, aiming to meet the growing demand for transparency and to improve AI systems’ usability and fairness when designed with user-centered principles.