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!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- 1
Introduction to Responsible AI Data Management
FreeLearn 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 dimensions50 xpDeveloping AI responsibly50 xpDimensions of responsible data100 xpResponsible AI metrics50 xpPlanning a responsible AI project50 xpResponsible data use100 xpFairness in AI projects100 xpChallenges of responsible AI50 xpTrade-offs in responsible AI100 xpProfessional duties and ethical conduct50 xp - 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.
Overview of data regulation50 xpData protection laws100 xpData regulation in a project100 xpData compliance50 xpData owner rights and compliance50 xpData use agreements (DUAs)100 xpThird-party licensing50 xpTypes of licenses100 xpLicensing agreements100 xpSelecting a license50 xpData governance and data management plan (DMP)50 xpData management100 xpComponents of a DMP50 xp - 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.
Data sources50 xpData source types100 xpData source and responsible dimensions50 xpData source limitations50 xpLimitations in data sources50 xpData sources and bias100 xpData source selection50 xpEvaluation of data sources100 xpData augmentation50 xpData integration50 xpData integration steps100 xpRisks and benefits of data integration50 xp - 4
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!
Data audit50 xpData audit in the project lifecycle50 xpNeed for data audit100 xpData validation50 xpDefining data validation50 xpData validation approaches50 xpData validation best practices50 xpSubgroup analysis100 xpData validation in the project50 xpPre-processing and bias100 xpBias mitigation50 xpMitigation strategies50 xpBias mitigation throughout project lifecycle100 xpConsequences of bias mitigation50 xpPost-deployment bias100 xpCongratulations!50 xp
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 Trackcollaborators
audio recorded by
prerequisites
Supervised Learning with scikit-learnMaria Prokofieva
See MoreLead 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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