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DataCamp

Responsible AI Data Management

via DataCamp

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Overview

Learn about responsible AI data management practices. Discover strategies covering all stages of an AI project to help you develop AI responsibly.

Responsible data management has become increasingly important nowadays. This course covers the basics of responsible data practices, including data acquisition, key regulations, main strategies to identify, and approaches to mitigate bias in data. By the end of this course, you will understand how to use data responsibly throughout all stages of an AI project and anticipate possible issues with deploying your AI model.

Syllabus

  • Introduction to Responsible AI Data Management
    • Prepare to master responsible data management in AI! To begin the course, you will learn about responsible data dimensions and some responsible AI metrics. Real-world examples will illustrate the challenges of balancing responsible AI with business factors and technical performance.
  • Regulation Compliance and Licensing
    • Data regulation is the cornerstone of the lawfulness of an AI project. This chapter delves into key regulations like GDPR and HIPAA, detailing compliance strategies for obtaining informed consent and establishing data-sharing agreements. Exploring various third-party licenses, you'll gain insight into selecting the right one for your dataset or model. Through crafting robust data governance strategies and management plans, you will master the basics of data regulation and compliance.
  • Data Acquisition
    • This chapter navigates the selection and integration of data sources within the context of responsible data practices. It highlights the importance of data origin, nature, and temporality, emphasizing legal compliance, diversity, and fairness. By exploring types of bias and their origins, we look at data fairness and representation to create a comprehensive dataset for modeling.
  • Data Validation and Bias Mitigation Strategies
    • Diving into the data, let's embark on a final quest to understand data audits, data validation, and bias mitigation. Data pre-processing and catching bias in modeling do not sound like fun, but let's streamline them with common approaches and trusted techniques!

Taught by

Maria Prokofieva

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