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Debiasing AI Using Amazon SageMaker

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Artificial intelligence (AI) can have deeply embedded bias. It’s the job of data scientists and developers to ensure their algorithms are fair, transparent, and explainable. This responsibility is critically important when building models that may determine policy—or shape the course of people’s lives. In this course, award-winning software engineer Kesha Williams explains how to debias AI with Amazon SageMaker. She shows how to use SageMaker to create a predictive-policing machine-learning model that integrates Rekognition and AWS DeepLens, creating a crime-fighting model that can “see” what’s happening in a live scene. By following the development process, you can learn what goes into making a model that doesn’t suffer from cultural prejudices. Kesha also discusses how to remove bias in training data, test a model for fairness, and build trust in AI by making models that are explainable.


1. Introduction
  • 001. Debiasing AI using Amazon SageMaker
  • 002. What you should know

  • 2. Crime-Fighting Case Study
  • 003. Predictive policing
  • 004. Overview of crime-fighting case study
  • 005. Architecture diagram
  • 006. Tools, services, and costs
  • 007. Terms and concepts
  • 008. Demo of Amazon SageMaker

  • 3. Building the Model via SageMaker
  • 009. What is SageMaker
  • 010. Machine learning process
  • 011. Inspect and visualize data
  • 012. Prepare the data
  • 013. Train the model
  • 014. Deploy the model

  • 4. Deploying and Testing the Model via DeepLens
  • 015. What is DeepLens
  • 016. Deploy model to AWS DeepLens
  • 017. Extend AWS DeepLens
  • 018. Retrieve attributes via AWS Rekognition
  • 019. Invoke the crime model
  • 020. Set up model alerts

  • 5. Explaining the Model
  • 021. What is explainable AI (XAI)
  • 022. Trust and transparency issues
  • 023. Making algorithms explainable

  • 6. Conclusion
  • 024. Next steps
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