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Autor(en): 
  • Tobias Baer
  • Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Juni 2019  
    Genre:  Schulbücher 
     
    Algorithm Analysis and Problem Complexity / Algorithms / B / Coding theory & cryptology / Coding theory and cryptology / Computer security / Cryptology / Data and Information Security / Data encryption / Data encryption (Computer science) / Data Mining / Data Mining and Knowledge Discovery / Data Storage Representation / Data structures (Computer science) / Expert systems / knowledge-based systems / Network Security / Professional and Applied Computing / Storage media & peripherals / Systems and Data Security / Theory of Computation
    ISBN:  9781484248843 
    EAN-Code: 
    9781484248843 
    Verlag:  Apress 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 15 mm 
    Gewicht:  400 gr 
    Seiten:  260 
    Zus. Info:  Paperback 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

    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 larger sociological impact of bias in the digital era.

      
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