Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists

Baer, Tobias

  • 出版商: Apress
  • 出版日期: 2019-06-08
  • 售價: $1,575
  • 貴賓價: 9.5$1,496
  • 語言: 英文
  • 頁數: 240
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484248848
  • ISBN-13: 9781484248843
  • 相關分類: Algorithms-data-structures
  • 立即出貨 (庫存=1)

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商品描述

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.

 

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

商品描述(中文翻譯)

人類的思維在演化中被設計成為為了生存而採取捷徑的。我們會草率下結論,是因為我們的大腦希望保護我們的安全。我們大部分的偏見都對我們有利,例如當我們感覺到一輛車朝我們的方向高速行駛時,我們立即移動,或者當我們決定不吃看起來已經壞掉的食物。然而,固有的偏見對工作環境和我們社區的決策產生了負面影響。儘管創建算法和機器學習試圖消除偏見,但它們毕竟是由人類創造的,因此容易受到我們所謂的算法偏見的影響。

在《了解、管理和預防算法偏見》一書中,作者Tobias Baer幫助您了解算法偏見的來源,以及如何作為企業用戶或監管者來管理它,以及數據科學如何防止偏見進入統計算法。Baer熟練地解釋了100多種自然偏見,例如確認偏見、穩定性偏見、模式識別偏見等等。算法偏見反映了這些人類傾向的特點。

儘管大多數關於算法偏見的著作都聚焦於危險,但這本積極、有趣的書的核心指向了一條保持偏見遠離甚至消除的道路。您將學到開發無偏算法的管理技巧,更快地檢測偏見的能力,以及創建無偏數據的知識。《了解、管理和預防算法偏見》是一本創新、及時且重要的書籍,值得放在您的書架上。無論您是經驗豐富的企業高管、數據科學家,還是一個熱衷者,現在是一個重要的時刻,需要對數字時代中偏見的更大社會影響有所了解。

您將學到什麼:

- 研究算法偏見的許多來源,包括現實世界中的認知偏見、有偏數據和統計偏差
- 了解算法偏見的風險,如何檢測它們,以及預防或管理它們的管理技巧
- 理解機器學習如何引入新的算法偏見來源,以及如何成為解決方案的一部分
- 熟悉數據科學家可以使用的特定統計技術,以檢測和克服算法偏見

這本書適合對日常運營中使用算法的公司的業務高管;開發算法的數據科學家(從學生到經驗豐富的從業者);關注算法偏見的合規官員;思考算法偏見對社會影響及可能的監管回應的政治家、記者和哲學家;以及關注自己可能受到算法偏見影響的消費者。

作者簡介

Tobias Baer is a data scientist, psychologist, and top management consultant with over 20 years of experience in risk analytics. Until June 2018, he was Master Expert and Partner at McKinsey & Co., Inc., where he built McKinsey's Risk Advanced Analytics Center of Competence in India in 2004, led the Credit Risk Advanced Analytics Service Line globally, and served clients in over 50 countries on topics such as the development of analytical decision models for credit underwriting, insurance pricing, and tax enforcement, as well as debiasing decisions. Tobias has been pursuing a research agenda around analytics and decision making both at McKinsey (e.g., on debiasing judgmental decisions and on leveraging machine learning to develop highly transparent predictive models) and at University of Cambridge, UK (e.g., the effect of mental fatigue on decision bias).

 

Tobias holds a PhD in finance from University of Frankfurt, an MPhil in psychology from University of Cambridge, an MA in economics from UWM, and has done undergraduate studies in business administration and law at University of Giessen. He started publishing as a teenager, writing about programming tricks for the Commodore C64 home computer in a German software magazine, and now blogs regularly on his LinkedIn page.

作者簡介(中文翻譯)

Tobias Baer是一位數據科學家、心理學家和頂尖管理顧問,擁有超過20年的風險分析經驗。在2018年6月之前,他是麥肯錫公司的高級專家和合夥人,於2004年在印度建立了麥肯錫的風險高級分析中心,全球領導信貸風險高級分析服務線,並為50多個國家的客戶提供服務,包括信貸核准、保險定價、稅務執法以及消除偏見的決策模型開發等領域。Tobias一直在麥肯錫和英國劍橋大學進行分析和決策研究,例如消除主觀決策的偏見和利用機器學習開發高度透明的預測模型等。

Tobias擁有法蘭克福大學的金融學博士學位,劍橋大學的心理學碩士學位,UWM的經濟學碩士學位,並在基森大學進行了工商管理和法律的本科學習。他從青少年時期開始發表文章,曾在一本德國軟件雜誌上寫關於Commodore C64家用電腦的編程技巧,現在定期在他的LinkedIn頁面上撰寫博客。