Explainable AI (XAI)

Also known as: XAI, Explainable AI, Model Interpretability, Transparent AI

Methods and techniques that make AI model decisions and predictions understandable and transparent to humans.

Explainable Artificial Intelligence (XAI) encompasses methods and techniques designed to make AI model outputs and decisions comprehensible, transparent, and justifiable to human users.

In market research, explainability is critical: when an AI model identifies a segment, predicts concept success, or recommends an optimal price, business teams need to understand why the model reached that conclusion in order to trust it and act on it.

Common XAI techniques: SHAP (SHapley Additive exPlanations) for understanding the weight of each variable in a prediction, LIME (Local Interpretable Model-agnostic Explanations), attention maps in NLP models, and saliency maps in vision models.

Emerging regulation (EU AI Act, LATAM privacy regulations) is making explainability a legal requirement in certain AI applications, not just a best practice.