TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Stodt, Jan
A1  - Reich, Christoph
T1  - Machine Learning Development Audit Framework: Assessment and Inspection of Risk and Quality of Data, Model and Development Process
JF  - International Journal of Computer and Information Engineering
N2  - 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.
KW  - Audit
KW  - Machine learning
KW  - Assessment
KW  - Metrics
Y1  - 2021
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:fn1-opus4-77950
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:fn1-opus4-77950
UR  - https://publications.waset.org/10011898/pdf
VL  - 15.2021
IS  - 3
SP  - 187
EP  - 193
ER  -