Following the principles of Hansei, here are two areas where I would approach a similar project at Netflix differently, based on the knowledge I’ve gained throughout this course:

1. Content Recommendation Algorithm Bias: While the project explored methods for creating personalized content recommendations for Netflix users, I would now place a greater emphasis on identifying and mitigating potential biases within the algorithms. The course highlighted the ethical considerations of AI and machine learning, particularly the risk of perpetuating existing biases in data sets. In the future, I would advocate for a more robust approach to bias detection and mitigation within the recommendation algorithms. This might involve working with data scientists to implement fairness checks and exploring techniques for diversifying training data sets.
2. User Privacy and Data Security: The project focused on user engagement and satisfaction through content recommendations. However, the course also emphasized the importance of user privacy and data security. In a future project, I would ensure a more balanced approach, considering not only user engagement but also robust data security measures. This might involve collaborating with security specialists to evaluate user data collection and storage practices, ensuring they comply with all relevant regulations and prioritize user privacy.

By adopting a more critical lens informed by the course content, I would strive to create content recommendation systems that are not only effective but also fair, unbiased, and respectful of user privacy.