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Data Bias

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May 1, 2024 3 minute read
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Data bias refers to systematic errors or distortions in data, which can lead to inaccurate or unreliable results in machine learning and data analysis. This topic is of particular interest to learners and students for several reasons:

Reasons for Learning about Data Bias

• **Increased Accuracy and Reliability:** Reducing data bias improves the accuracy and reliability of data-driven decision-making, which is crucial for ethical and trustworthy AI applications.

Path to Data Bias

Take the first step.
We've curated six courses to help you on your path to Data Bias. Use these to develop your skills, build background knowledge, and put what you learn to practice.
Sorted from most relevant to least relevant:

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Reading list

We've selected five 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 Data Bias.
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The The Reinforcement Learning Workshop
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Offers a comprehensive introduction to algorithmic fairness, covering the ethical and societal implications of bias in machine learning algorithms, with a focus on developing fair and equitable algorithmic systems.
The Reinforcement Learning Workshop: Learn how to...
Kindle Edition
$$
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Machine Learning
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Addresses the challenges of explaining machine learning models, discussing different techniques for model interpretation, and highlighting the importance of explainability for addressing data bias and ensuring transparency in decision-making.
Machine Learning: The Art and Science of Algorithms...
Kindle Edition
$$
Machine Learning: ECML 2001: 12th European...
Paperback
$$$
Machine Learning: The Art and Science of Algorithms...
Paperback
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Machine Learning and Knowledge Discovery in...
Paperback
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Machine Learning and Knowledge Discovery in...
Paperback
$$$
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The Ethical Algorithm
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Explores the ethical considerations involved in the development and deployment of AI systems, focusing on how to design algorithms that can promote fairness, transparency, and social good.
The Ethical Algorithm: The Science of Socially...
Hardcover
$$
(Español) El algoritmo ético. La ciencia del diseño de...
Kindle Edition
$$
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Machine Learning Risk Assessments in Criminal...
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Offers a compact and accessible overview of bias and fairness in machine learning, presenting key concepts, frameworks, and techniques for detecting and mitigating bias, and ensuring the fairness of data-driven systems.
Machine Learning Risk Assessments in Criminal...
Hardcover
$$$
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The Atomic Human
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Examines the ethical implications of AI and machine learning, discussing the principles of data ethics, the challenges of ensuring fairness and accountability, and the role of data governance in building trustworthy AI systems.
The Atomic Human
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The Atomic Human
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