Explainable artificial intelligence is governed by four fundamental principles, as identified by data-science experts at the National Institute of Standards and Technology (NIST). Let’s delve into these principles:
- Explanation: AI systems should offer clear explanations for their actions, leaving no room for ambiguity. They must elaborate on how they process data, make decisions, and reach specific outcomes. For instance, a credit scoring machine learning model should be capable of explaining why it approved or rejected an application, shedding light on pivotal factors like credit history or income level.
- Meaningful: Explanations provided by AI systems need to be understandable and meaningful for humans, especially non-experts. Complex technical jargon serves only to confuse and erode trust. Consider the healthcare sector, notorious for its technobabble. If an AI system is used for diagnosing diseases, it should present its findings in a way that both doctors and patients can grasp, focusing on key factors that contribute to the diagnosis, such as high blood pressure or obesity. Failing to do so may hinder doctors’ ability to prescribe appropriate treatment, leading to potentially dire consequences.
- Explanation Accuracy: Providing explanations is significant, but it is equally crucial for these explanations to be accurate. How can we ensure this accuracy? Methods like feature importance ranking can highlight the most influential variables in the decision-making process. Additionally, techniques such as local interpretable model-agnostic explanations (LIME) and SHapley additive exPlanations (SHAP) effectively offer both local and global explanations of a model’s behavior.
- Knowledge Limits: Just like humans, AI systems have limits. Awareness of these limitations and uncertainties is vital. They should only operate under the conditions for which they were designed and when they attain sufficient confidence in their outputs, as rightly stated by NIST.
Understanding these four principles is crucial to harnessing the power of explainable AI effectively.