We may earn an affiliate commission when you visit our partners.

Algorithmic Bias

Save
May 1, 2024 Updated July 6, 2025 17 minute read

What is Algorithmic Bias?

Algorithmic bias occurs when an algorithm produces results that are unfair or discriminatory. This can happen for a variety of reasons, including:

  • The data used to train the algorithm is biased.
  • The algorithm itself is biased.
  • The way the algorithm is used is biased.

Algorithmic bias can have a wide range of negative consequences. For example, it can lead to people being denied jobs, credit, or housing. It can also lead to people being unfairly targeted by law enforcement or the criminal justice system.

Why is Algorithmic Bias a Problem?

Algorithmic bias is a problem because it can have a significant impact on individuals and society as a whole. For example, algorithmic bias can lead to:

  • Discrimination: Algorithmic bias can lead to people being denied jobs, credit, or housing based on their race, gender, or other protected characteristics.
  • Unfairness: Algorithmic bias can lead to people being treated unfairly by the criminal justice system or other institutions.
  • Harm to individuals: Algorithmic bias can cause people to lose their jobs, their homes, or even their lives.

Path to Algorithmic Bias

Take the first step.
We've curated 12 courses to help you on your path to Algorithmic Bias. Use these to develop your skills, build background knowledge, and put what you learn to practice.
Sorted from most relevant to least relevant:

Share

Help others find this page about Algorithmic Bias: by sharing it with your friends and followers:

Reading list

We've selected six 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 Algorithmic Bias.
Focusing on the ethical implications of AI and algorithms, this book explores how to design and assess algorithms that promote fairness and justice, aligning well with the topic's emphasis on bias mitigation.
Specifically examines algorithmic bias in search engines and its impact on perpetuating racial discrimination, offering a unique perspective on a specific aspect of the topic.
While not directly addressing algorithmic bias, this book provides a strong foundation in the principles of machine learning and AI, which are essential for understanding the root causes and mitigation strategies for algorithmic bias.
Explores the societal implications of algorithmic bias, particularly in the context of criminal justice and social welfare systems, providing a broader perspective on the impact of bias beyond technical discussions.
Provides a comprehensive overview of AI and its potential impact on society, including discussions on algorithmic bias and its consequences, making it a valuable resource for understanding the broader context of the topic.
This paper provides a concise overview of algorithmic decision making and bias, covering key concepts and challenges, making it suitable for beginners or those seeking a quick introduction.
Table of Contents
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser