TY - CHAP
T1 - Is Siri a Little Bit Racist? Recognizing and Confronting Algorithmic Bias in Emerging Media
AU - Austin, Michael L
PY - 2019/2/26
Y1 - 2019/2/26
N2 - This chapter considers the power of algorithms in our lives, and how—even unintentionally—racial bias can be introduced into algorithms. Algorithms are particularly important in the software that our computers and smart devices use to solve problems, help us make choices, and work more efficiently. In computer science, an algorithm is “any well-defined computational procedure that takes some value, or set of values, as input and produces some value, or set of values, as output. Algorithms help online search engines rank results based on relevance and popularity by monitoring the activity of millions of users. The chapter addresses algorithmic bias in new and emerging media, examine its implications for potentially vulnerable groups, and discusses algorithmic accountability. Hiring people of various races, genders, sexual orientations, and dis/abilities will provide perspectives that may help identify a problem in an algorithm or its training data and prevent unintentional bias before it takes root.
AB - This chapter considers the power of algorithms in our lives, and how—even unintentionally—racial bias can be introduced into algorithms. Algorithms are particularly important in the software that our computers and smart devices use to solve problems, help us make choices, and work more efficiently. In computer science, an algorithm is “any well-defined computational procedure that takes some value, or set of values, as input and produces some value, or set of values, as output. Algorithms help online search engines rank results based on relevance and popularity by monitoring the activity of millions of users. The chapter addresses algorithmic bias in new and emerging media, examine its implications for potentially vulnerable groups, and discusses algorithmic accountability. Hiring people of various races, genders, sexual orientations, and dis/abilities will provide perspectives that may help identify a problem in an algorithm or its training data and prevent unintentional bias before it takes root.
UR - http://dx.doi.org/10.4324/9781351630276-55
U2 - 10.4324/9781351630276-55
DO - 10.4324/9781351630276-55
M3 - Chapter
SN - 9781351630276
BT - Race/Gender/Class/Media
ER -