Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute
This work addresses the situation where a black-box model with good predictive performance is chosen over its interpretable competitors, and we show interpretability is still achievable in this case. Our solution is to find an interpretable substitute on a subset of data where the black-box model is overkill or nearly overkill while leaving the rest to the black-box. This transparency is obtained at minimal cost or no cost of the predictive performance. Under this framework, we develop a Hybrid Rule Sets (HyRS) model that uses decision rules to capture the subspace of data where the rules are as accurate or almost as accurate as the black-box provided. To train a HyRS, we devise an efficient search algorithm that iteratively finds the optimal model and exploits theoretically grounded strategies to reduce computation. Our framework is agnostic to the black-box during training. Experiments on structured and text data show that HyRS obtains an effective trade-off between transparency and interpretability.
Code (1)
Tasks
Decision MakingInterpretable Machine LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitio…
Interpretable Machine LearningmodelModel-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models
Driven by an increasing need for model interpretability, interpretable models have become strong competitors for black-box models in many real applications. In this paper, we propose a novel type of model where interpret…
Mimic: An adaptive algorithm for multivariate time series classification
Time series data are valuable but are often inscrutable. Gaining trust in time series classifiers for finance, healthcare, and other critical applications may rely on creating interpretable models. Researchers have previ…
ClassificationTime SeriesTime Series AnalysisTime Series ClassificationOn Interpretability and Similarity in Concept-Based Machine Learning
Machine Learning (ML) provides important techniques for classification and predictions. Most of these are black-box models for users and do not provide decision-makers with an explanation. For the sake of transparency or…
BIG-bench Machine LearningClusteringGeneral ClassificationInterpretable Machine LearningThe Disparate Effects of Partial Information in Bayesian Strategic Learning
We study how partial information about scoring rules affects fairness in strategic learning settings. In strategic learning, a learner deploys a scoring rule, and agents respond strategically by modifying their features …
Fairnessscoring rule