May 1, 2024
4 minute read
Bias in Machine Learning is a significant issue that can lead to inaccurate or unfair predictions. Understanding and addressing bias is crucial for building responsible and ethical machine learning models. Bias can arise from various sources, including the data used for training, the algorithms employed, and the assumptions made during model development.
Sources of Bias in Machine Learning
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Find a path to becoming a Bias in Machine Learning. Learn more at:
OpenCourser.com/topic/tdhx1a/bias
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Bias in Machine Learning.
Explores the ethical implications of machine learning and provides guidance on how to develop fair and ethical AI systems. It is written by two leading experts in the field and is suitable for readers with a basic understanding of machine learning.
Examines the use of AI systems in the criminal justice system and its disproportionate impact on the poor and people of color. It argues that AI systems can perpetuate and amplify existing social inequalities. It valuable resource for anyone who is interested in the ethical and social implications of AI.
Examines the use of AI systems in the financial and information industries and its implications for privacy and democracy. It argues that AI systems can be used to manipulate people and control information. It valuable resource for anyone who is interested in the ethical and social implications of AI.
Provides a non-technical overview of bias in machine learning. It is written in a clear and concise style and is suitable for readers with no prior knowledge of machine learning.
Examines the use of AI systems in the surveillance and data collection industries and its implications for privacy and democracy. It argues that AI systems can be used to track and control people. It valuable resource for anyone who is interested in the ethical and social implications of AI.
Examines the use of AI systems in the surveillance and data collection industries and its implications for privacy and democracy. It argues that AI systems can be used to manipulate people and control information. It valuable resource for anyone who is interested in the ethical and social implications of AI.
Examines the causes and consequences of bias in data. It argues that bias in data can lead to biased AI systems. It valuable resource for anyone who is interested in the ethical and social implications of AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/tdhx1a/bias
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