Algorithmic Fairness and Bias in Machine Learning Systems
نویسندگان
چکیده
In recent years, research into and concern over algorithmic fairness bias in machine learning systems has grown significantly. It is vital to make sure that these are fair, impartial, do not support discrimination or social injustices since algorithms becoming more prevalent decision-making processes across a variety of disciplines. This abstract gives general explanation the idea fairness, difficulties posed by systems, different solutions problems. Algorithmic crucial issues this regard demand attention academics, practitioners, policymakers. Building fair unbiased uphold equality prevent requires addressing biases training data, creating fairness-aware algorithms, encouraging transparency interpretability, diversity inclusivity.
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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: E3S web of conferences
سال: 2023
ISSN: ['2555-0403', '2267-1242']
DOI: https://doi.org/10.1051/e3sconf/202339904036