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Building Data Science Solutions with Anaconda

You're reading from  Building Data Science Solutions with Anaconda

Product type Book
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Pages 330 pages
Edition 1st Edition
Languages
Author (1):
Dan Meador Dan Meador
Profile icon Dan Meador

Table of Contents (16) Chapters

Preface Part 1: The Data Science Landscape – Open Source to the Rescue
Chapter 1: Understanding the AI/ML landscape Chapter 2: Analyzing Open Source Software Chapter 3: Using the Anaconda Distribution to Manage Packages Chapter 4: Working with Jupyter Notebooks and NumPy Part 2: Data Is the New Oil, Models Are the New Refineries
Chapter 5: Cleaning and Visualizing Data Chapter 6: Overcoming Bias in AI/ML Chapter 7: Choosing the Best AI Algorithm Chapter 8: Dealing with Common Data Problems Part 3: Practical Examples and Applications
Chapter 9: Building a Regression Model with scikit-learn Chapter 10: Explainable AI - Using LIME and SHAP Chapter 11: Tuning Hyperparameters and Versioning Your Model Other Books You May Enjoy

Defining bias versus discrimination

Let's start by making sure we have a clear understanding of the two components in the context of AI – bias and discrimination. There are different aspects to each of these components and it's important to understand the difference between them.

Bias in AI/ML

AI/ML bias is when models that have been created show favor toward certain groups or categories that doesn't reflect the actual state of the world.

Bias is inevitable in any model and in itself can be harmless. Let's say you are going to author a paper about the most popular foods and do some analysis on them. To do so, you collect data from your friends and family as to their preferences. In addition to this, think about the three foods that you would reply with. Are there any vegetables in there? Any Ethiopian foods? Anything from Turkey? Perhaps not.

This is a form of bias; unless you take a perfectly even sample size of people across the world, you are...

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