%0 Journal Article
	%A Jan Stodt and  Christoph Reich
	%D 2021
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 171, 2021
	%T Machine Learning Development Audit Framework: Assessment and Inspection of Risk and Quality of Data, Model and Development Process
	%U https://publications.waset.org/pdf/10011898
	%V 171
	%X 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.
	%P 187 - 193