Congratulations!

1. Congratulations!

Well done! This is the final video of this course. Let's review our journey briefly.

2. Chapter 1: Responsible data dimensions

We started with an overview of responsible data dimensions and how they relate to AI project steps.

3. Chapter 2: Lawfulness and compliance

We moved on to overview compliance and we learned about different types of data laws, including country and industry specific regulations, such as HIPAA. We talked about the foundation of legal compliance which is respecting data ownership rights, such as individual users and organizations. We talked about complying with informed consent rules for personal data, Data Use Agreements for cross-business data sharing and licensing for broader data use.

4. Chapter 3: Data sources and integration

We revisited the foundation of any AI project - data sources concept and talked about their limitations and integration in a responsible way. We reviewed the connection between data diversity and representation with bias and fairness of the data itself as well as the fairness of the model outcomes.

5. Chapter 4: Validation and bias

In the final chapter we moved to more advanced stages of the AI project and talked about bias and fairness in preprocessing, modeling and post-deployment stages. We specifically aligned this discussion with fairness assessment as the ultimate goal of responsible AI project.

6. What's next?

We have only touched the tip of the iceberg - responsible AI is such an enormous area! But don't let that iceberg be your Titanic. Remember, in the world of AI, you're the captain! So, navigate and explore the AI ocean responsibly! Steering clear of disasters and sailing towards a future of ethical AI exploration!

7. Congratulations!

Congratulations!

This exercise is part of the course

Responsible AI Data Management

IntermediateSkill Level

4.8+

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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.

Exercise 1: Responsible data dimensions Exercise 2: Developing AI responsibly Exercise 3: Dimensions of responsible data Exercise 4: Responsible AI metrics Exercise 5: Planning a responsible AI project Exercise 6: Responsible data use Exercise 7: Fairness in AI projects Exercise 8: Challenges of responsible AI Exercise 9: Trade-offs in responsible AI Exercise 10: Professional duties and ethical conduct

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.

Exercise 1: Overview of data regulation Exercise 2: Data protection laws Exercise 3: Data regulation in a project Exercise 4: Data compliance Exercise 5: Data owner rights and compliance Exercise 6: Data use agreements (DUAs)Exercise 7: Third-party licensing Exercise 8: Types of licenses Exercise 9: Licensing agreements Exercise 10: Selecting a license Exercise 11: Data governance and data management plan (DMP)Exercise 12: Data management Exercise 13: Components of a DMP

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.

Exercise 1: Data sources Exercise 2: Data source types Exercise 3: Data source and responsible dimensions Exercise 4: Data source limitations Exercise 5: Limitations in data sources Exercise 6: Data sources and bias Exercise 7: Data source selection Exercise 8: Evaluation of data sources Exercise 9: Data augmentation Exercise 10: Data integration Exercise 11: Data integration steps Exercise 12: Risks and benefits of data integration

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!

Exercise 1: Data audit Exercise 2: Data audit in the project lifecycle Exercise 3: Need for data audit Exercise 4: Data validation Exercise 5: Defining data validation Exercise 6: Data validation approaches Exercise 7: Data validation best practices Exercise 8: Subgroup analysis Exercise 9: Data validation in the project Exercise 10: Pre-processing and bias Exercise 11: Bias mitigation Exercise 12: Mitigation strategies Exercise 13: Bias mitigation throughout project lifecycle Exercise 14: Consequences of bias mitigation Exercise 15: Post-deployment bias Exercise 16: Congratulations!

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