Veri kaynağı ve sorumlu boyutlar

Veri kaynaklarının kökeni ve niteliği, yasallık, önyargı ve adillik gibi sorumlu boyutları şekillendirerek AI modellerinin bütünlüğünü etkiler.

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Sorumlu AI Veri Yönetimi

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Sorumlu AI Veri Yönetimi

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4.7+

Kursa Ücretsiz Başlayın

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: Veri kaynakları Exercise 2: Veri kaynağı türleri Exercise 3: Veri kaynağı ve sorumlu boyutlar

Geçerli Egzersiz

Exercise 4: Veri kaynağı sınırlamaları Exercise 5: Veri kaynaklarındaki kısıtlar Exercise 6: Veri kaynakları ve önyargı Exercise 7: Veri kaynağı seçimi Exercise 8: Veri kaynaklarının değerlendirilmesi Exercise 9: Veri zenginleştirme Exercise 10: Veri entegrasyonu Exercise 11: Veri entegrasyonu adımları Exercise 12: Veri entegrasyonunun riskleri ve faydaları

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!