Quantitative effects of explainability modalities on user perceptions

Determine how different forms of explainability in AI systems quantitatively shape user perceptions.

Background

The paper reviews prior Explainable AI (XAI) literature and notes that much of it has been qualitative or theoretical, highlighting a lack of quantitative evidence connecting specific explanation types to measurable user outcomes.

Although the paper presents a preliminary quantitative experiment comparing several explanation conditions in a loan approval context, the authors explicitly identify a broader, field-level uncertainty about the quantitative impact of different explainability modalities on user perceptions.