Limitações em fontes de dados

Você está desenvolvendo um modelo para melhorar resultados de saúde pública e tem dados de pesquisas nacionais de saúde, prontuários de pacientes de hospitais e estatísticas de uso de um app de saúde móvel.

Quais das opções a seguir são verdadeiras sobre possíveis limitações nas fontes de dados deste caso? Selecione as três respostas que se aplicam.

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Gestão Responsável de Dados em IA

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Este exercicio faz parte do curso

Gestão Responsável de Dados em IA

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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: Fontes de dados Exercise 2: Tipos de fontes de dados Exercise 3: Fonte de dados e dimensões de responsabilidade Exercise 4: Limitações da fonte de dados Exercise 5: Limitações em fontes de dados

Exercicio Atual

Exercise 6: Fontes de dados e viés Exercise 7: Seleção de fontes de dados Exercise 8: Avaliação de fontes de dados Exercise 9: Aumento de dados Exercise 10: Integração de dados Exercise 11: Etapas de integração de dados Exercise 12: Riscos e benefícios da integração de dados

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!