Interpretable Machine Learning

COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations

Interpretable machine learning models trade off accuracy for simplicity to make explanations more readable and easier to comprehend. Drawing from cognitive psychology theories in graph comprehension, we formalize readability as visual cognitive …

Designing Theory-Driven User-Centric Explainable AI

From healthcare to criminal justice, artificial intelligence (AI) is increasingly supporting high-consequence human decisions. This has spurred the field of explainable AI (XAI). This paper seeks to strengthen empirical application-specific …

Trends and trajectories for explainable, accountable and intelligible systems: An HCI research agenda

Advances in artificial intelligence, sensors and big data management have far-reaching societal impacts. As these systems augment our everyday lives, it becomes increasingly important for people to understand them and remain in control. We …