**Technical AI Governance Challenge:**

Technical AI Governance Challenge:

You are a senior data scientist at a leading online music streaming company, with over 200 million users worldwide. Your organization uses a proprietary AI-based music recommendation system to personalize playlists for each user. The AI model is trained on a large dataset of user behavior, song features, and metadata.

Problem Statement:

The AI model has recently started to exhibit a bias in recommending songs from male artists to female users, and vice versa. This bias is not explicitly programmed into the model, and you suspect it may be caused by an imbalance in the training data, which is predominantly composed of user data from the United States and Europe.

Constraints:

1. You have a strict data protection policy in place to ensure that user data is anonymized and handled with the utmost care.
2. You cannot access the underlying source code of the AI model, as it is proprietary and owned by a third-party vendor.
3. You have limited computational resources and cannot re-train the entire AI model from scratch.
4. You must maintain the current performance of the recommendation system, which is critical for user engagement and retention.

Technical Requirements:

1. Develop a post-hoc analysis method to identify the root cause of the bias in the AI model.
2. Propose a solution to mitigate the bias without re-training the entire model or modifying the proprietary source code.
3. Provide a data-driven justification for your solution, along with metrics to measure its effectiveness.
4. Evaluate the impact of your solution on the performance of the recommendation system.

Deliverables:

Please submit a technical report (max. 10 pages) outlining your approach, methodology, and results. Include code samples and visualization of the analysis.

Submission Guidelines:

Please submit your report to [your email address] by December 15, 2025. Late submissions will not be accepted.

Evaluation Criteria:

Submissions will be evaluated based on technical merit, creativity, and effectiveness in addressing the problem. A selection of the best submissions will be presented at an upcoming AI governance conference.

Get ready to challenge the status quo and push the boundaries of AI governance!

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