The Balancing Act of Policies in Developing Machine Learning Explanations

kaestner@cs.cmu.edu (J. Tjaden)
Thu, 11 Dec 2025 09:54:38 GMT

Jacob Tjaden. The Balancing Act of Policies in Developing Machine Learning Explanations. In Proceedings of the International Conference on Software Engineering (Companion) (ICSE-SRC), pages 237--238, New York, NY: ACM Press, 2025. ICSE student research competition, first place.

Machine learning models are often criticized as opaque from a lack of transparency in their decision-making process. This study examines how policy design impacts the quality of explanations in ML models. We conducted a classroom experiment with 124 participants and analyzed the effects of policy length and purpose on developer compliance with policy requirements. Our results indicate that while policy length affects engagement with some requirements, policy purpose has no effect, and explanation quality is generally poor. These findings highlight the challenge of effective policy development and the importance of addressing diverse stakeholder perspectives within explanations.