Detecting and Mitigating AI Bias and Hallucinations

As artificial intelligence becomes increasingly integrated into our daily lives, understanding its limitations, such as generating incorrect or biased information, is crucial for effective and responsible use. This course will equip you with the foundational knowledge and practical approaches to recognize, analyze, and begin to mitigate AI hallucinations, inaccuracies, and inherent biases, fostering a more critical and informed engagement with AI technologies. What you'll learn: * Learn the fundamental concepts of AI hallucinations, inaccuracies, and various forms of bias. * Understand common causes and manifestations of AI-generated errors and misleading information. * Apply systematic methods for identifying and evaluating bias in AI outputs and underlying data. * Explore foundational strategies for mitigating bias and improving the reliability of AI systems. * Understand how data quality and prompt engineering influence the accuracy and fairness of AI outputs. * Grasp the ethical implications and best practices for responsible AI development and usage. The course begins with essential definitions and theoretical underpinnings, progressing through practical identification techniques and culminating in discussions on ethical considerations and mitigation strategies for AI outputs. This course is designed for anyone new to AI who wants to understand its limitations and learn how to interact with AI systems more effectively and critically, with no prior experience required. Start building your understanding of responsible AI engagement today.