Curs
Responsible AI Data Management
IntermediarNivel de competențe
Actualizat 05.2026
TheoryArtificial Intelligence1 h16 videoclipuri51 Exerciții3,500 XP9,317Certificat de realizare
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Încearcă pentru afaceriDescrierea cursului
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!Cerințe prealabile
Supervised Learning with scikit-learn1
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.
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.
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.
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!
Responsible AI Data Management
Curs finalizat
Obține diploma de absolvire
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Alătură-te celor peste 19 de milioane de cursanți și începe Responsible AI Data Management astăzi!
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Continuă cu GoogleArată mai multe opțiunisau
Continuând, accepți Termenii de utilizare, Politica de confidențialitate și faptul că datele tale sunt stocate în SUA.
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