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Understand, Manage, and Prevent Algorithmic Bias - A Guide for Business Users and Data Scientists (Paperback, 1st ed.)
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Understand, Manage, and Prevent Algorithmic Bias - A Guide for Business Users and Data Scientists (Paperback, 1st ed.)
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Are algorithms friend or foe? The human mind is evolutionarily
designed to take shortcuts in order to survive. We jump to
conclusions because our brains want to keep us safe. A majority of
our biases work in our favor, such as when we feel a car speeding
in our direction is dangerous and we instantly move, or when we
decide not take a bite of food that appears to have gone bad.
However, inherent bias negatively affects work environments and the
decision-making surrounding our communities. While the creation of
algorithms and machine learning attempts to eliminate bias, they
are, after all, created by human beings, and thus are susceptible
to what we call algorithmic bias. In Understand, Manage, and
Prevent Algorithmic Bias, author Tobias Baer helps you understand
where algorithmic bias comes from, how to manage it as a business
user or regulator, and how data science can prevent bias from
entering statistical algorithms. Baer expertly addresses some of
the 100+ varieties of natural bias such as confirmation bias,
stability bias, pattern-recognition bias, and many others.
Algorithmic bias mirrors-and originates in-these human tendencies.
Baer dives into topics as diverse as anomaly detection, hybrid
model structures, and self-improving machine learning. While most
writings on algorithmic bias focus on the dangers, the core of this
positive, fun book points toward a path where bias is kept at bay
and even eliminated. You'll come away with managerial techniques to
develop unbiased algorithms, the ability to detect bias more
quickly, and knowledge to create unbiased data. Understand, Manage,
and Prevent Algorithmic Bias is an innovative, timely, and
important book that belongs on your shelf. Whether you are a
seasoned business executive, a data scientist, or simply an
enthusiast, now is a crucial time to be educated about the impact
of algorithmic bias on society and take an active role in fighting
bias. What You'll Learn Study the many sources of algorithmic bias,
including cognitive biases in the real world, biased data, and
statistical artifact Understand the risks of algorithmic biases,
how to detect them, and managerial techniques to prevent or manage
them Appreciate how machine learning both introduces new sources of
algorithmic bias and can be a part of a solution Be familiar with
specific statistical techniques a data scientist can use to detect
and overcome algorithmic bias Who This Book is For Business
executives of companies using algorithms in daily operations; data
scientists (from students to seasoned practitioners) developing
algorithms; compliance officials concerned about algorithmic bias;
politicians, journalists, and philosophers thinking about
algorithmic bias in terms of its impact on society and possible
regulatory responses; and consumers concerned about how they might
be affected by algorithmic bias
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