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.

Responsible data dimensions

Developing AI responsibly

Dimensions of responsible data

Responsible AI metrics

Planning a responsible AI project

Responsible data use

Fairness in AI projects

Challenges of responsible AI

Trade-offs in responsible AI

Professional duties and ethical conduct

Introduction to Responsible AI Data Management

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.

Overview of data regulation

Data protection laws

Data regulation in a project

Data compliance

Data owner rights and compliance

Data use agreements (DUAs)

Third-party licensing

Types of licenses

Licensing agreements

Selecting a license

Data governance and data management plan (DMP)

Data management

Components of a DMP

Regulation Compliance and Licensing

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 sources

Options

Primary quantitative static data 

Primary qualitative dynamic data 

Secondary quantitative dynamic data

Data source types

Data source and responsible dimensions

Data source limitations

Limitations in data sources

Historical bias

Selection bias

Sampling bias

Data sources and bias

Data source selection

Data sources evaluation steps

Evaluation of data sources

Data augmentation

Data integration

Data integration steps

Risks and benefits of data integration

Data Acquisition

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!

Data audit

Data audit in the project lifecycle

Need for data audit

Data validation

Defining data validation

Data validation approaches

Data validation best practices

Subgroup analysis

Data validation in the project

Pre-processing and bias

Bias mitigation

Mitigation strategies

Bias mitigation throughout project lifecycle

Consequences of bias mitigation

Post-deployment bias

Congratulations!

Data Validation and Bias Mitigation Strategies

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.

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.
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<h2>Learn About Regulatory Compliance and Licensing</h2> 

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.
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<h2>Source and Use Data Responsibly</h2>

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.
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<h2>Audit Your Data</h2>

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!

Supervised Learning with scikit-learn

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

Responsible AI Data Management

Associate AI Engineer for Data Scientists

Evaluate data sources for their quality and ability to contribute to a more balanced dataset

Identify common variables and standardize data formats

Calculate descriptive stats to assess the balance and representation of different demographics 

Develop metadata with detailed descriptions of data sources

Data integration steps

Responsible AI Data Management

Hands-on interactive exercise