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.

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.

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.

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!