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Responsible AI Data Management

Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.

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Course Description

Artificial Intelligence (AI) and data are everywhere. Their growing presence in our everyday lives makes it even more important to ensure we responsibly manage the data throughout our AI projects, whether at work or in our personal projects. This conceptual course will explore the fundamental theory behind responsible AI data management, such as security and transparency, before exploring licensing, acquisition, and validation.

Learn About Regulatory Compliance and Licensing

With an understanding of the fundamental theory, you'll use this knowledge to assess your compliance and licensing requirements (seeking legal counsel where appropriate). You'll learn about some of the most significant data regulations like HIPAA and GDPR, some of the most common license types, and how to use a data management plan to ensure your AI project always stays compliant.

Source and Use Data Responsibly

Responsible data practices also involve how and where you source your data. You'll understand whether or not a source is ethical, any limitations it might have, and how to integrate data from different sources.

Audit Your Data

Finally, you'll learn about data auditing and how to apply data validation and mitigation strategies to ensure your data stays bias-free. With all of these skills, you'll be able to critically assess and responsibly manage the data in any AI project. What's more, you can use these skills for any future data project, making you feel adaptable and prepared for whatever comes your way!
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In the following Tracks

Associate AI Engineer for Data Scientists

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  1. 1

    Introduction to Responsible AI Data Management

    Free

    Learn about the fundamental theory behind responsible data management in AI. You’ll review key dimensions such as security, transparency, fairness, and more before conceptualizing the metrics and challenges associated with these dimensions and understanding how to balance responsible AI with other business and technical requirements.

    Play Chapter Now
    Responsible data dimensions
    50 xp
    Developing AI responsibly
    50 xp
    Dimensions of responsible data
    100 xp
    Responsible AI metrics
    50 xp
    Planning a responsible AI project
    50 xp
    Responsible data use
    100 xp
    Fairness in AI projects
    100 xp
    Challenges of responsible AI
    50 xp
    Trade-offs in responsible AI
    100 xp
    Professional duties and ethical conduct
    50 xp
  2. 2

    Regulation Compliance and Licensing

    Data regulation is essential to the legality of any AI project. Learn about key regulations, third-party licenses, and compliance strategies for informed consent and data-sharing agreements (with legal counsel). Finally, you'll learn about developing robust data governance strategies and management plans to ensure your project remains compliant throughout its lifecycle.

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  3. 3

    Data Acquisition

    Navigate through the responsible selection and integration of data sources by understanding the importance of data origin, nature, and temporality, emphasizing legal compliance, diversity, and fairness. By exploring types of bias and their origins, you’ll look at data fairness and representation to create a comprehensive dataset for modeling.

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For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Associate AI Engineer for Data Scientists

Go To Track

collaborators

Collaborator's avatar
James Chapman
Collaborator's avatar
Jasmin Ludolf
Collaborator's avatar
Francesca Donadoni

audio recorded by

Maria Prokofieva's avatar
Maria Prokofieva

prerequisites

Supervised Learning with scikit-learn
Maria Prokofieva HeadshotMaria Prokofieva

Lead ML Engineer

I am a Lead ML engineer at the Mitchell Institute, Vic. With PhD in Computer Science and CPA qualification, I work in the area of deep learning applications in business and healthcare. I love LLMs and fight for responsible AI.
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