Recognizing Hallucinations, Inaccuracies, and Bias in AI

  • 7 videos | 27m 17s
  • Includes Assessment
  • Earns a Badge
  • Certification CPE
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In this course, you will gain a focused understanding of the challenges related to hallucinations, inaccuracies, and biases in AI-generated content, including foundational concepts, identification strategies, and mitigation approaches to help you recognize and address these issues responsibly.


  • Discover the key concepts covered in this course
    Recognize the implications of ai-generated hallucinations in image and text generation
    Identify how the generative ai process can unintentionally lead to biased or inaccurate outputs
    Identify the four steps in the process of reviewing generative ai outputs to ensure output reliability
  • Identify approaches and techniques for mitigating biases in ai models
    Recognize the importance of promoting ethical and responsible use of ai technologies
    Reflect on what you've learned


  • 1m 20s
    Our lives are closely intertwined with AI-generated content, making it important for us to understand its pitfalls. In this course, you'll learn about the challenges related to generative AI, including hallucinations, inaccuracies, and biases. FREE ACCESS
  • 4m 25s
    Generative AI is growing in popularity and people are quick to embrace it. But sometimes, all isn't as it seems. In this topic, you'll learn why you need to be cautious about accepting images and text created by generative AI at face value, and educate yourself on how this technology can be misused. FREE ACCESS
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    3.  Identifying Inaccuracies and Biases
    Generative AI is transforming our world in countless ways but like most tools, it has its drawbacks. In this topic, you'll learn how to recognize the potential for AI systems to produce inaccurate or biased results, understand the consequences, and mitigate these issues. FREE ACCESS
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    4.  Evaluating Output Reliability
    5m 38s
    Given the rate at which generative AI is growing in popularity, it's essential to evaluate AI-generated content for inaccuracies, biases, and limitations. In this topic, you'll learn how to recognize and address potential issues effectively, and optimize the utilization of AI technology. FREE ACCESS
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    5.  Exploring Bias Mitigation Strategies
    4m 26s
    Bias in AI models is a significant challenge, creating the potential to deliver biased outputs that can cause unfair or harmful outcomes. In this topic, you'll learn how to use data pre-processing, algorithmic adjustments, and data post-processing to mitigate this issue. FREE ACCESS
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    6.  Promoting Ethical and Responsible Use
    4m 37s
    As the global AI market continues to evolve, it becomes increasingly important to promote the ethical and responsible use of AI. In this topic, you'll learn how to leverage transparency, accountability, and monitoring to mitigate the risks associated with generative AI. FREE ACCESS
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    7.  Let's Review
    This topic summarizes the key concepts covered in the course Recognizing Hallucinations, Inaccuracies and Bias. FREE ACCESS


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