A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning

Understand

Research in interpretable machine learning proposes different computational and human subject approaches to evaluate model saliency explanations.

• These approaches measure different qualities of explanations to achieve diverse goals in designing interpretable machine learning systems.
• In this paper, we propose a human attention benchmark for image and text domains using multi-layer human attention masks aggregated from multiple human annotators.
• We then present an evaluation study to evaluate model saliency explanations obtained using Grad-cam and LIME techniques.