Commenced in January 2007
Paper Count: 32297
Machine Learning Development Audit Framework: Assessment and Inspection of Risk and Quality of Data, Model and Development Process
Authors: Jan Stodt, Christoph Reich
Abstract:The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.
Keywords: Audit, machine learning, assessment, metrics.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 583
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