You're reading from Machine Learning Infrastructure and Best Practices for Software Engineers

Product typeBook

Published inJan 2024

Reading LevelIntermediate

PublisherPackt

ISBN-139781837634064

Edition1st Edition

Languages

Python

Concepts

Machine Learning

Author (1)

Miroslaw Staron

References

Donald, A., et al., Bias Detection for Customer Interaction Data: A Survey on Datasets, Methods, and Tools. IEEE Access, 2023.
Bellamy, R.K., et al., AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943, 2018.
Zhang, Y., et al. Introduction to AI fairness. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 2020.
Alves, G., et al. Reducing unintended bias of ml models on tabular and textual data. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). 2021. IEEE.
Raza, S., D.J. Reji, and C. Ding, Dbias: detecting biases and ensuring fairness in news articles. International Journal of Data Science and Analytics, 2022: p. 1-21.

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Machine Learning Infrastructure and Best Practices for Software Engineers

Published in: Jan 2024Publisher: PacktISBN-13: 9781837634064

Author (1)

Miroslaw Staron

Miroslaw Staron is a professor of Applied IT at the University of Gothenburg in Sweden with a focus on empirical software engineering, measurement, and machine learning. He is currently editor-in-chief of Information and Software Technology and co-editor of the regular Practitioner's Digest column of IEEE Software. He has authored books on automotive software architectures, software measurement, and action research. He also leads several projects in AI for software engineering and leads an AI and digitalization theme at Software Center. He has written over 200 journal and conference articles.
Read more about Miroslaw Staron

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Author (1)

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