An Explanatory Model Steering System for Collaboration between Domain Experts and AI

Abstract

With the increasing adoption of Artificial Intelligence (AI) systems in high-stake domains, such as healthcare, effective collaboration between domain experts and AI is imperative. To facilitate effective collaboration between domain experts and AI systems, we introduce an Explanatory Model Steering system that allows domain experts to steer prediction models using their domain knowledge. The system includes an explanation dashboard that combines different types of data-centric and model-centric explanations and allows prediction models to be steered through manual and automated data configuration approaches. It allows domain experts to apply their prior knowledge for configuring the underlying training data and refining prediction models. Additionally, our model steering system has been evaluated for a healthcare-focused scenario with 174 healthcare experts through three extensive user studies. Our findings highlight the importance of involving domain experts during model steering, ultimately leading to improved human-AI collaboration.

Demo paper accepted for ACM UMAP 2024

References in corpus (5)

• Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations
• EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations
• Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
• Towards Directive Explanations: Crafting Explainable AI Systems for Actionable Human-AI Interactions
• DeepSeer: Interactive RNN Explanation and Debugging via State Abstraction

Cited by in corpus (2)

• Show Me How: Benefits and Challenges of Agent-Augmented Counterfactual Explanations for Non-Expert Users
• Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems