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Found 6 papers, 1 papers with code
Locally Invariant Explanations: Towards Causal Explanations through Local Invariant Learning
Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level.
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability.
Blockchain Enabled Trustless API Marketplace
Typically, a data owner (API provider) develops a model, often over proprietary data, and leverages the infrastructure services of a cloud vendor for hosting and serving API requests.
Model Extraction Warning in MLaaS Paradigm
In this work, we present a cloud-based extraction monitor that can quantify the extraction status of models by observing the query and response streams of both individual and colluding adversarial users.
