Bir projede veri düzenlemeleri

Birleşik Krallık'ta faaliyet gösteren bir sağlık şirketinde çalışıyorsun ve AB ile ABD'deki sağlayıcılar için bir AI teşhis aracı geliştiriyorsun. Hukuk ekibiyle bir toplantı ayarladın ve şimdi içgörü kazanmak için ilgili veri yasalarını araştırıyorsun.

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Sorumlu AI Veri Yönetimi

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Sorumlu AI Veri Yönetimi

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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: Veri düzenlemelerine genel bakış Exercise 2: Veri koruma yasaları Exercise 3: Bir projede veri düzenlemeleri

Geçerli Egzersiz

Exercise 4: Veri uyumluluğu Exercise 5: Veri sahibi hakları ve uyumluluk Exercise 6: Veri kullanım anlaşmaları (DUA'lar)Exercise 7: Üçüncü taraf lisanslama Exercise 8: Lisans türleri Exercise 9: Lisans anlaşmaları Exercise 10: Bir lisans seçme Exercise 11: Veri yönetişimi ve veri yönetim planı (DMP)Exercise 12: Veri yönetimi Exercise 13: Bir DMP'nin Bileşenleri

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: 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!