Rom humans to the algorithm itself. Fighting bias What You'll LearnStudy the many sources of algorithmic bias including cognitive biases in the real world biased data and statistical artifactUnderstand the risks of algorithmic biases how to detect them and managerial techniues to prevent or manage themAppreciate how machine learning both introduces new sources of algorithmic bias and can be a part of a solutionBe familiar with specific statistical techniues a data scientist can use to detect and overcome algorithmic bias Who This Book is ForBusiness 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 bi.
Good core book for understanding. Are algorithms friend or foeThe human mind is evolutionarily designed to take shortcuts in order to survive We jump to conclusions because our brains want to eep 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 biasIn 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 addre.
The source of bias in algorithms Sses 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 learningWhile most writings on algorithmic bias focus on the dangers the core of this positive fun book points toward a path where bias is ept at bay and even eliminated You'll come away with managerial techniues to develop unbiased algorithms the ability to detect bias uickly and nowledge 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.