An Analysis of Regularized Approaches for Constrained Machine Learning
| Autor: | Michela Milano, Andrea Borghesi, Federico Baldo, Michele Lombardi |
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| Přispěvatelé: | Heintz F., Milano M., O'Sullivan B., Lombardi, Michele, Baldo, Federico, Borghesi, Andrea, Milano, Michela |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Science - Artificial Intelligence Machine Learning (stat.ML) 02 engineering and technology Machine learning computer.software_genre Regularization (mathematics) GeneralLiterature_MISCELLANEOUS Machine Learning (cs.LG) Machine Learning Statistics - Machine Learning 020204 information systems Regularization 0202 electrical engineering electronic engineering information engineering business.industry Trustworthiness Artificial Intelligence (cs.AI) Constraints Domain knowledge 020201 artificial intelligence & image processing Artificial intelligence business Machine Learning Constrained Machine Learning Regularization Methods computer |
| Zdroj: | Trustworthy AI-Integrating Learning, Optimization and Reasoning ISBN: 9783030739584 TAILOR |
| DOI: | 10.5281/zenodo.3944312 |
| Popis: | Regularization-based approaches for injecting constraints in Machine Learning (ML) were introducedto improve a predictive model via expert knowledge. Given the recent interest in ethical and trustworthy AI, however, several works are resorting to these approaches for the (conceptually different) task of enforcing desired properties over a ML model, e.g. fairness. When regularized methods are used to enforce constraints, a typical approach consists in adjusting the regularizer multipliersuntil a suitable compromise between accuracy and constraint satisfaction is reached(e.g. a discrimination index becomes sufficiently low). This approach enables the use of traditional training algorithms, at the cost of having to search over the space of possible multipliers.Though the method is known to work well in many practical cases, the process has been subject to little general analysis. With this note, we make a preliminary step in this direction, providing a more systematic overview of the strengths and (in particular) potential weaknesses of this class of approaches. |
| Databáze: | OpenAIRE |
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