Understanding Consumer Preferences for Explanations Generated by XAI Algorithms

Understand

Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers.

• While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there has not yet been sufficient consideration of customers' preferences for various types and formats of explanations.
• We discuss theoretically and study empirically people's preferences for explanations of algorithmic decisions.
• We focus on three main attributes that describe automatically-generated explanations from existing XAI algorithms (format, complexity, and specificity), and capture differences across contexts (online targeted advertising vs.