Summary  
This chapter covers programming techniques for ensuring ethical AI use, such as bias detection and mitigation, transparency interfaces, and human-in-the-loop controls.

General domain of usage  
Marketing

AI predicts user preferences, but too much tracking becomes intrusive.

**+** Stores suggesting relevant products.</br>
**-** Hyper-targeted messages based on private details.

Users must understand how personalization works and have control over it.

Users appreciate helpful experiences but dislike manipulation or secrecy.

**+** Companies that clearly explain how AI works and allow user control build loyalty.

Transparency creates trust; manipulation destroys it.

AI reflects the quality of its training data. Biased data creates unfair outcomes.

**-** Hiring AI rejecting women due to biased résumé data.
**-** Marketing tools excluding certain groups.

Requires fairness testing, reviewing data sources, and keeping humans in the loop.

Quelles sont les causes du biais algorithmique ?

Comprendre les principales règles et risques du marketing digital. Se concentrer sur les bases de la confidentialité des données, les politiques des plateformes, l'utilisation éthique des données et la gestion de la sécurité de la marque, y compris la prévention et la réponse aux risques et crises potentiels.

Utilisation Éthique des Données et IA dans le Marketing

Principaux défis éthiques