Fairness, explainability, privacy, and robustness for trustworthy algorithmic decision-making-Reference-Cited by-同舟云学术

Fairness, explainability, privacy, and robustness for trustworthy algorithmic decision-making

Published:2023 Issue: Volume: Page:61-95
ISSN:
Container-title:Big Data Analytics in Chemoinformatics and Bioinformatics
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Author:

Majumdar Subhabrata

Publisher

Elsevier

Reference205 articles.

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3. Adebayo, J.A., 2016. FairML: Toolbox for Diagnosing Bias in Predictive Modeling (Master’s thesis). MIT, United States. https://github.com/adebayoj/fairml.

4. Alvarez-Melis, D., Daumé, H., Vaughan, J.W., Wallach, H., 2019. Weight of evidence as a basis for human-oriented explanations. In: Human-Centric Machine Learning (HCML) Workshop@ NeurIPS 2019. arXiv:1910.13503.

5. Amershi, S., Chickering, M., Drucker, S.M., et al., 2015. ModelTracker: redesigning performance analysis tools for machine learning. In: Proc. the 2015 CHI conference on human factors in computing systems (CHI), pp. 337–346.

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