Responsible AI Data Management

Overview

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

Handling data responsibly is critical, particularly in Artificial Intelligence (AI). This conceptual course will teach you the fundamentals of responsible data practices, including data acquisition, key regulations, and data validation and bias mitigation strategies. You can apply these skills to use critical thinking on any data project, ensuring you have a successful, responsible, and compliant project from start to finish.

Syllabus

Introduction to Responsible AI Data Management

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.

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.

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.

Data Validation and Bias Mitigation Strategies

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!