Pra-pemrosesan dan bias

Sebuah aplikasi AI berfokus pada kesehatan bertujuan untuk menyesuaikan saran nutrisi dan merekomendasikan resep dengan menggunakan data pengguna, termasuk usia, jenis kelamin, dan tujuan kesehatan. Aplikasi ini menganalisis masukan dan perilaku pengguna serta memberikan rekomendasi yang disesuaikan yang beradaptasi seiring waktu.

Latihan ini merupakan bagian dari kursus

Manajemen Data AI yang Bertanggung Jawab

Latihan interaktif langsung

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Latihan ini merupakan bagian dari kursus

Manajemen Data AI yang Bertanggung Jawab

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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 data Exercise 2: Audit data dalam siklus hidup proyek Exercise 3: Kebutuhan akan audit data Exercise 4: Validasi data Exercise 5: Mendefinisikan validasi data Exercise 6: Pendekatan validasi data Exercise 7: Praktik terbaik validasi data Exercise 8: Analisis subkelompok Exercise 9: Validasi data dalam proyek Exercise 10: Pra-pemrosesan dan bias

Latihan Saat Ini

Exercise 11: Mitigasi bias Exercise 12: Strategi mitigasi Exercise 13: Mitigasi bias sepanjang siklus hidup proyek Exercise 14: Konsekuensi mitigasi bias Exercise 15: Bias pascadeploy Exercise 16: Selamat!