Check Your Data Source Track Your Data Lineage Know Your Tools Use Automated Visualizations Impact = Decision Do Reality Checks Limit Your Assumptions Think Like a Science Teacher Solve for Missing Data Take Two Perspectives and Call Me in the Morning Chapter 18: Bias In, Bias Out (and Other Pitfalls) A Pitfalls Overview Relying on Racist Algorithms Following a Flawed Model for Repeat Offenders Using A Sexist Hiring Algorithm Redlining Loans Leaning on Irrelevant Information Falling Victim to Framing Foibles Being Overconfident Lulled by Percentages Dismissing with Prejudice
10 Index
List of Tables
1 Chapter 15TABLE 15-1: The Four Structured Decision-Making Models
List of Illustrations
1 Chapter 3FIGURE 3-1: How fast are these spinning again?
2 Chapter 4FIGURE 4-1: The inverted V.
3 Chapter 6FIGURE 6-1: Is a picture always worth a thousand words?
4 Chapter 9FIGURE 9-1: Machine learning has trouble accurately recognizing objects among s...FIGURE 9-2: Feast, an open source feature store.FIGURE 9-3: Comparing what is with what could be.
5 Chapter 10FIGURE 10-1: Looking at RapidMiner, a data science software platform.
6 Chapter 12FIGURE 12-1: A set of PowerPoint decision tree templates.FIGURE 12-2: A Microsoft Excel SWOT template.
7 Chapter 14FIGURE 14-1: Causal – Scenarios, as shown in the Google Workspace Marketplace.FIGURE 14-2: The home of what-if on the Excel Ribbon.FIGURE 14-3: The Excel Scenario Manager Wizard.FIGURE 14-4: The WhatIf add-in, as shown in Google Workspace Marketplace.FIGURE 14-5: Excel's Goal Seek feature.FIGURE 14-6: The Goal Seek add-in, as shown in Google Workspace Marketplace.
8 Chapter 15FIGURE 15-1: The Coggle brainstorming tool.FIGURE 15-2: Brainpartner's causal loop tool.FIGURE 15-3: Visme's structural thinking tool.
Guide
1 Cover
4 Table of Contents
6 Index
7 About the Book Author
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