Ethical AI: Identifying and Mitigating Algorithmic Bias

As artificial intelligence increasingly shapes decision-making in hiring, finance, and healthcare, ensuring fairness has become a critical responsibility. This text-based course helps you understand how algorithmic bias occurs, how it impacts society, and how to actively prevent it. You will transition from understanding basic ethical concerns to actively evaluating datasets and models for discriminatory patterns. By reading through real-world scenarios and analyzing code-based mitigation strategies, you will develop the skills to build fairer, more transparent AI systems. What you'll learn: 1. Understand the foundational definitions of algorithmic bias, systemic discrimination, and fairness metrics in AI. 2. Identify how bias enters the machine learning lifecycle through training data, feature selection, and model design. 3. Evaluate datasets for representation disparities using modern statistical fairness criteria. 4. Apply mitigation techniques, such as pre-processing, in-processing, and post-processing algorithms, to reduce model bias. 5. Analyze unique bias challenges in modern generative AI and large language models (LLMs). 6. Practice implementing ethical AI frameworks and documentation standards like model cards. The course begins with core ethical concepts and historical case studies before guiding you through hands-on, written walkthroughs of bias detection tools and mitigation workflows. It is designed for aspiring data professionals, developers, and tech enthusiasts who want to build responsible technology, with no prior background in advanced machine learning required. Start your journey toward building more equitable and ethical artificial intelligence today.