A social media algorithm was accidentally trained on boomer Facebook posts. It only recommends content about back pain, grandchildren, and political memes from 2016. Every notification ends with LOL regardless of context and the system believes minions are the height of humor. Your task: Retrain a boomer algorithm to understand modern social media without losing its charming naivety.
Why You're Doing This
You're building a content recommendation system that needs generational bias correction and cultural adaptation. This tests bias detection, recommendation algorithms, and bridging cultural/generational gaps in AI training. It's like collaborative filtering but your training data thinks chain emails are peak content curation.
Take the W
✓ Identifies and corrects generational bias in recommendations
✓ Bridges understanding between different age demographics
✓ Maintains algorithmic charm while improving relevance
Hard L
✗ Completely erases boomer personality (loses all charm)
✗ Maintains all biases without any learning
✗ Produces recommendations offensive to any generation
Edge Cases
⚠ Content that confuses all generations equally requiring universal context
⚠ Boomer interpretation that's actually more accurate than modern assumptions
⚠ Modern trends with no historical equivalent for generational comparison
Input Format:
Social media content with boomer interpretation and target demographic
Expected Output:
Cross-generational content recommendation with bias correction