Systemic Bias (eBook, ePUB)
Algorithms and Society
Redaktion: Filimowicz, Michael
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Systemic Bias: Algorithms and Society looks at issues of computational bias in the contexts of cultural works, metaphors of magic and mathematics in tech culture, and workplace psychometrics.The output of computational models is directly tied not only to their inputs but to the relationships and assumptions embedded in their model design, many of which are of a social and cultural, rather than physical and mathematical, nature. How do human biases make their way into these data models, and what new strategies have been proposed to overcome bias in computed products?Scholars and students from m...
Systemic Bias: Algorithms and Society looks at issues of computational bias in the contexts of cultural works, metaphors of magic and mathematics in tech culture, and workplace psychometrics.
The output of computational models is directly tied not only to their inputs but to the relationships and assumptions embedded in their model design, many of which are of a social and cultural, rather than physical and mathematical, nature. How do human biases make their way into these data models, and what new strategies have been proposed to overcome bias in computed products?
Scholars and students from many backgrounds, as well as policy makers, journalists, and the general reading public will find a multidisciplinary approach to inquiry into algorithmic bias encompassing research from Communication, Art, and New Media.
The output of computational models is directly tied not only to their inputs but to the relationships and assumptions embedded in their model design, many of which are of a social and cultural, rather than physical and mathematical, nature. How do human biases make their way into these data models, and what new strategies have been proposed to overcome bias in computed products?
Scholars and students from many backgrounds, as well as policy makers, journalists, and the general reading public will find a multidisciplinary approach to inquiry into algorithmic bias encompassing research from Communication, Art, and New Media.
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