The Who in XAI: How AI Background Shapes Perceptions of AI Explanations

TL;DR

A mixed-methods study of how two different groups—people with and without AI background—perceive different types of AI explanations finds that both groups showed unwarranted faith in numbers for different reasons and each group found value in different explanations beyond their intended design.

Abstract

Explainability of AI systems is critical for users to take informed actions. Understanding "who" opens the black-box of AI is just as important as opening it. We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations. Quantitatively, we share user perceptions along five dimensions. Qualitatively, we describe how AI background can influence interpretations, elucidating the differences through lenses of appropriation and cognitive heuristics. We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design. Carrying critical implications for the field of XAI, our findings showcase how AI generated explanations can have negative consequences despite best intentions and how that could lead to harmful manipulation of trust. We propose design interventions to mitigate them.

Figures (2)

• Figure 1: The three robots navigating the task environment and explaining their actions. From left to right, the robots and their colors are: Rationale-Generation (in blue), Action-Declaring (in purple), and Numerical-Reasoning (in green). In the screenshot, each robot is taking the same action, but they are explaining it differently. The explanation text accompanying each robot is taken verbatim from the videos participants watched. To improve legibility, the text has been remastered to a higher resolution.
• Figure 2: Distributions of rankings for each robot, in each dimension, separated by participant group