The Promise and Peril of Human Evaluation for Model Interpretability

Understand

Transparency, user trust, and human comprehension are popular ethical motivations for interpretable machine learning.

• In support of these goals, researchers evaluate model explanation performance using humans and real world applications.
• This alone presents a challenge in many areas of artificial intelligence.
• In this position paper, we propose a distinction between descriptive and persuasive explanations.