Conséquences de l’atténuation des biais

Vous travaillez sur une application d’approbation de prêts basée sur l’IA et appliquez des stratégies d’atténuation des biais pour garantir la parité entre les genres dans le processus de présélection. Vous rédigez une note exposant les conséquences. Lesquelles des options devez-vous inclure ? Sélectionnez les deux réponses qui s’appliquent

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<cours>Gestion responsable des données pour l’IA</cours>

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Cet exercice fait partie du cours

<cours>Gestion responsable des données pour l’IA</cours>

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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 des données Exercise 2: Audit des données dans le cycle de vie du projet Exercise 3: Besoin d’un audit de données Exercise 4: Validation des données Exercise 5: Définir la validation des données Exercise 6: Approches de validation des données Exercise 7: Bonnes pratiques de validation des données Exercise 8: Analyse par sous-groupes Exercise 9: Validation des données dans le projet Exercise 10: Prétraitement et biais Exercise 11: Réduction des biais Exercise 12: Stratégies de mitigation Exercise 13: Mitigation des biais tout au long du cycle de vie du projet Exercise 14: Conséquences de l’atténuation des biais

Exercice actuel

Exercise 15: Biais après le déploiement Exercise 16: Félicitations !