Ethical Use of AI in Content Recommendation

At Netflix, the primary goal is subscriber retention through high engagement, but we must avoid the ethical trap of creating addictive feedback loops or isolating echo chambers. To solve this, I propose a Dual-Objective Optimization framework. First, we acknowledge that pure engagement maximization leads to short-term gains but long-term brand damage. My strategy involves modifying the ranking algorithm's loss function. Instead of optimizing solely for predicted watch time, we introduce a 'Well-being Penalty' variable. This penalizes recommendations that lead to excessive binge-watching without natural stopping points or those that repeatedly serve ideologically similar content, thereby widening the user's horizon. Technically, this means injecting diversity into the top-k results. For example, after a user watches three intense dramas in a row, the system could prioritize a lighter comedy or a documentary to provide cognitive relief. We would also implement a 'Pause & Reflect' nudge if a user exceeds their average daily watch time, offering them control rather than forcing endless scrolling. Finally, we measure success not just by hours watched, but by a composite metric combining retention rates with user-reported satisfaction and reduced churn due to fatigue. This ensures Netflix remains a trusted entertainment partner rather than a source of digital exhaustion.