Understanding AI Bias: Ethical Frameworks and Algorithmic Fairness

Artificial intelligence shapes everything from hiring decisions to healthcare, yet these systems often inherit and amplify human prejudices. Understanding how algorithmic bias occurs is the first step toward building a fairer, more equitable technological future. This course guides you through the foundational concepts of AI ethics, exploring how data collection, model training, and societal structures contribute to biased outcomes. You will gain the critical thinking skills needed to identify algorithmic bias and understand modern methods for mitigation. What you'll learn: Understand the core terminology of artificial intelligence, machine learning, and algorithmic bias; Analyze how historical and systemic prejudices are encoded into training datasets; Explore ethical frameworks and fairness metrics used to evaluate AI systems; Identify real-world impacts of biased algorithms across diverse industries; Examine modern mitigation strategies, including diverse data sourcing and algorithmic auditing. The course begins with essential definitions of AI and ethical principles before progressing to real-world case studies and practical frameworks for assessing algorithmic fairness. You will study written explanations and conceptual exercises designed to build your analytical skills. Designed entirely for beginners, this course requires no prior programming, mathematical, or technical background. Start reading today to champion ethical practices in the rapidly evolving world of artificial intelligence.