Algorithmic Bias

Algorithmic bias refers to systematic and unfair discrimination in automated systems, particularly in machine learning and artificial intelligence, where algorithms produce outcomes that favor or disadvantage certain groups based on attributes like race, gender, or age. It arises from biased data, flawed model design, or unintended consequences in deployment, leading to ethical and social harms such as unfair hiring, lending, or policing decisions. This concept is critical in ensuring that technology promotes equity and does not perpetuate or amplify existing societal inequalities.

Also known as: AI Bias, Machine Learning Bias, Data Bias, Algorithmic Discrimination, Bias in Algorithms

Why learn Algorithmic Bias?

Developers should learn about algorithmic bias to build fair and responsible AI systems, especially when creating applications in sensitive domains like finance, healthcare, criminal justice, or employment, where biased outcomes can have severe real-world impacts. Understanding this concept helps in identifying and mitigating biases during data collection, model training, and evaluation phases, ensuring compliance with ethical guidelines and regulations such as GDPR or AI ethics frameworks. It is essential for fostering trust and inclusivity in technology.

Learning Resources

• docs: Algorithmic Bias in Machine Learning: A Survey
• book: Fairness and Machine Learning
• course: Coursera: AI Ethics and Bias
• docs: Google's Responsible AI Practices
• video: YouTube: What is Algorithmic Bias?