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4017. Making Black-Boxes Explainable and Fair Through Mathematical Optimization
Invited abstract in session MB-27: On Mathematical Optimization for Explainable and Fair Machine Learning, stream Mathematical Optimization for XAI.
Monday, 10:30-12:00Room: 047 (building: 208)
Authors (first author is the speaker)
| 1. | Thomas Halskov
|
| Copenhagen Business School | |
| 2. | Emilio CARRIZOSA
|
| IMUS - Instituto de Matemáticas de la Universidad de Sevilla | |
| 3. | Dolores Romero Morales
|
| Copenhagen Business School |
Abstract
Machine Learning has increasingly been used for algorithmic decision-making, often through what are considered "black-boxes", i.e., models which do not provide an explanation for their outputs. In this talk, we consider the task of explaining the output and decisions from a given "black-box" to provide an interpretable explanation. Additionally, we tackle fairness concerns that might arise from using "black-box" models, where discrimination can be present. Our approach aims to give a local explanation for an output, while taking into account global fairness and accuracy.
Keywords
- Machine Learning
- Mathematical Programming
- Artificial Intelligence
Status: accepted
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