Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY

Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making

Abstract: The widespread adoption of algorithmic decision-making systems has brought about the necessity to interpret the reasoning behind these decisions. The majority of these systems are complex black box models, and auxiliary models are often used to approximate and then explain their behavior. However, recent research suggests that such explanations are not overly accessible to lay users with no specific expertise in machine learning and this can lead to an incorrect interpretation of the underlying model. In this paper, we show that a predictive and interactive model based on causality is inherently interpretable, does not require any auxiliary model, and allows both expert and non-expert users to understand the model comprehensively. To demonstrate our method we developed Outcome Explorer, a causality guided interactive interface, and evaluated it by conducting think-aloud sessions with three expert users and a user study with 18 non-expert users. All three expert users found our tool to be comprehensive in supporting their explanation needs while the non-expert users were able to understand the inner workings of a model easily.

Teaser: The below shows the Outcome Explorer visual interface by which users can interpret the causal model and interactively perform comparative counterfactual what-if queries:

The GUI elements are: (A) Interactive causal DAG showing causal relations between variables. Each node includes two circular knobs (green and orange) to facilitate profile comparisons. The edge thickness and color depict the effect size and type of each edge. (B) Sample selection panel. (C) A biplot showing the position of green and orange profiles compared to nearest neighbors. (D) A line chart to track the model outcome and to go back and forth between feature configurations. (E) Realism meter allowing users to determine how common a profile is compared to other samples in the dataset.

Video: Watch it to get a quick overview:

Paper: N. Hoque, K. Mueller, "Ooutcome-Explorer: A causality guided interactive visual interface for interpretable algorithmic decision making," IEEE Trans. on Visualization and Computer Graphics, (to appear) 2021 PDF | SUPPL

Funding: NSF grant IIS-1527200 and 1941613.