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2804. Explainability and Interpretability in Mathematical Optimization
Invited abstract in session WB-3: Interpretable Optimization Methods and Applications, stream Data Science Meets Optimization.
Wednesday, 10:30-12:00Room: 1005 (building: 202)
Authors (first author is the speaker)
| 1. | Michael Hartisch
|
| Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg |
Abstract
The evolution of mathematical programming has revolutionized our ability to address once-deemed intractable real-world problems on a large scale. Despite the efficiency of modern optimization techniques, the reluctance to accept provably optimal solutions persists, largely attributed to the perception of optimization software as a black box by many stakeholders. While well-understood by the scientific community, this lack of transparency poses a barrier to practitioners. We advocate for a paradigm shift by emphasizing the importance of incorporating aspects of interpretability and explainability in mathematical optimization. By clarifying the concepts of explainability and interpretability, we aim to bridge the gap between the advanced techniques understood by scientists and the accessibility required by practitioners. We will showcase initial steps taken in this direction and engage in a discussion on potential future directions.
Keywords
- Analytics and Data Science
- Knowledge Engineering and Management
- Artificial Intelligence
Status: accepted
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