Value alignment in AI design

AI systems always embed values through their design choices, whether designers intend this or not. Value alignment makes sure these embedded values match what users and stakeholders actually care about, not just what's easy to measure. The process starts by clearly naming important values, like privacy, fairness, efficiency, autonomy, or accessibility, instead of leaving them unspoken.

In real situations, values often clash: think about a streaming service that recommends TV shows. The service wants to give you shows you'll love, but also be transparent about how it makes those picks. To create better recommendations, the service tracks not just what you watch but how you watch, whether you finish shows, rewatch scenes, or what time of day you watch. While this improves recommendations, explaining all these factors would be overwhelming, so the service simply says "Recommended for you" without sharing all the details. The service has chosen personalization over transparency.

Good value alignment would make this tradeoff clear, setting boundaries on what data is collected and deciding when being transparent matters most. Without this careful approach, AI systems tend to focus on easy-to-measure things like watch time, which might not reflect what people truly value. Clear value alignment gives teams a consistent way to make decisions throughout development.