Titlebook: Understand, Manage, and Prevent Algorithmic Bias; A Guide for Business Tobias Baer Book 2019 Tobias Baer 2019 algorithmic bias.decision bia

Amenable

How Algorithms Debias Decisions the most prevalent biases are. In this chapter, aimed primarily at readers who do not have experience in building algorithms themselves, I will explain how an algorithm works. More specifically, I will show how a . algorithm works and how it thereby can . human bias; in later chapters you then can

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The Model Development Processl in understanding the many ways biases can creep into algorithms. Also, seasoned data scientists may want to briefly glance at this chapter so that they are aware of my mental frame and terminology since I will be referencing both frequently going forward. One note on terminology: with the advent o

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古代

Data Scientists’ Biasesect data to refute them. Very often, however, there is the data required to keep biases out of the algorithm—but somehow the data scientist lets a bias slip through nevertheless. This chapter looks more closely at this cause of algorithmic bias.

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MOCK

Algorithmic Biases and Social Mediathmic bias occurs: the choice of posts shown to social media users. In doing so, I achieve two objectives: this serves as a case study that shows how the various biases discussed so far can interact and reinforce each other, and it illustrates how algorithmic bias can be dynamic. Rather than set in

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Chandelier

How to Detect Algorithmic Biasest nicely, "99 percent of all statistics only tell 49 percent of the story." As a result, a lot of rubbish is said and done because of meaningless numbers showing up in some report. Even if no bad intentions are involved, a poorly calculated or interpreted number can seriously mislead you. This chapt