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

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樓主: MOTE
51#
發(fā)表于 2025-3-30 08:55:27 | 只看該作者
Tobias Baerng. The July 1961 freeze occurred midway and contributed not only to the development of a second stage of the postwar settlement (extracted from the innate wisdom of the first), but also to the evolution of a new one, a conscious attempt to reorder the state’s priorities, and the role of government
52#
發(fā)表于 2025-3-30 13:56:47 | 只看該作者
Tobias Baerollers are commonly used for LFC systems in power industry. But the dynamic behaviors in the presence of variations in load changes with different operating conditions are needed to be improved. This paper proposes Particle Swarm Optimization (PSO) based Load Frequency Control for improving the dyna
53#
發(fā)表于 2025-3-30 19:22:13 | 只看該作者
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發(fā)表于 2025-3-31 00:20:41 | 只看該作者
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發(fā)表于 2025-3-31 04:10:13 | 只看該作者
56#
發(fā)表于 2025-3-31 06:00:51 | 只看該作者
Options for Decision-MakingIn the first two parts of this book, you learned that the mechanics of algorithms expose them to many potential sources of bias, that biases are real and at times exceedingly harmful, and that algorithmic biases very often originate in real-world biases.
57#
發(fā)表于 2025-3-31 10:39:19 | 只看該作者
Managerial Strategies for Correcting Algorithmic BiasI still remember my shock when I interviewed a Chief Risk Officer of a bank about the key risk drivers she considered when underwriting credit, and one of the first things she said was that homosexuals are "obviously" risky (and hence should be avoided as borrowers).
58#
發(fā)表于 2025-3-31 17:00:28 | 只看該作者
Tobias BaerTeaches the many sources of algorithmic bias and shows the holistic measures you can use to manage and prevent bias.Provides practical, proven techniques to effectively combat and eliminate bias.Addre
59#
發(fā)表于 2025-3-31 18:45:27 | 只看該作者
60#
發(fā)表于 2025-3-31 22:45:30 | 只看該作者
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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