Analisi per sottogruppi

In un progetto che mira a prevedere i tassi di approvazione dei prestiti, il team ha terminato il pre-processing e decide di condurre un'analisi per sottogruppi per garantire l'equità tra i gruppi per genere e livello di reddito.

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Gestione responsabile dei dati per l'AI

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Gestione responsabile dei dati per l'AI

IntermediárioNível de habilidade

4.7+

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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: Audit dei dati Exercise 2: Data audit nel ciclo di vita del progetto Exercise 3: Necessità di un data audit Exercise 4: Convalida dei dati Exercise 5: Definire la data validation Exercise 6: Approcci alla data validation Exercise 7: Buone pratiche per la validazione dei dati Exercise 8: Analisi per sottogruppi

Esercizio in corso

Exercise 9: Validazione dei dati nel progetto Exercise 10: Pre-processing e bias Exercise 11: Mitigazione del bias Exercise 12: Strategie di mitigazione Exercise 13: Mitigazione del bias lungo il ciclo di vita del progetto Exercise 14: Conseguenze della mitigazione del bias Exercise 15: Bias post-deployment Exercise 16: Congratulazioni!