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
