Managing Bias in Machine Learning Projects
نویسندگان
چکیده
This paper introduces a framework for managing bias in machine learning (ML) projects. When ML-capabilities are used decision making, they frequently affect the lives of many people. However, can lead to low model performance and misguided business decisions, resulting fatal financial, social, reputational impacts. provides an overview potential biases corresponding mitigation methods each phase well-established process CRISP-DM. Eight distinct types 25 were identified through literature review allocated six phases reference synthesized way. Furthermore, some mitigated different as occur. Our helps create clarity these multiple relationships, thus assisting project managers avoiding biased ML-outcomes.
منابع مشابه
Appendix : Machine Learning Bias Versus Statistical Bias
is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...
متن کاملAppendix : Machine Learning Bias Versus Statistical Bias
is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...
متن کاملAppendix : Machine Learning Bias Versus Statistical Bias
is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...
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ژورنال
عنوان ژورنال: Lecture notes in information systems and organisation
سال: 2021
ISSN: ['2195-4976', '2195-4968']
DOI: https://doi.org/10.1007/978-3-030-86797-3_7