EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations

• URL: http://arxiv.org/abs/2402.00491v1
• Date: Thu, 1 Feb 2024 10:57:00 GMT
• Title: EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations
• Authors: Aditya Bhattacharya, Simone Stumpf, Lucija Gosak, Gregor Stiglic, Katrien Verbert
• Abstract summary: This research investigates the influence of data-centric and model-centric explanations in interactive machine-learning systems. We conducted studies with healthcare experts to explore the impact of different explanations on trust, understandability and model improvement. Our results reveal the insufficiency of global model-centric explanations for guiding users during data configuration.
• Score: 5.132827811038276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explanations in interactive machine-learning systems facilitate debugging and improving prediction models. However, the effectiveness of various global model-centric and data-centric explanations in aiding domain experts to detect and resolve potential data issues for model improvement remains unexplored. This research investigates the influence of data-centric and model-centric global explanations in systems that support healthcare experts in optimising models through automated and manual data configurations. We conducted quantitative (n=70) and qualitative (n=30) studies with healthcare experts to explore the impact of different explanations on trust, understandability and model improvement. Our results reveal the insufficiency of global model-centric explanations for guiding users during data configuration. Although data-centric explanations enhanced understanding of post-configuration system changes, a hybrid fusion of both explanation types demonstrated the highest effectiveness. Based on our study results, we also present design implications for effective explanation-driven interactive machine-learning systems.

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