Algorithmic amplifiers like social media and AI chatbots play an increasingly important role in how our society operates. But despite being built with the intention to be useful and valuable, these algorithmic systems have demonstrated a fundamental misalignment with their users’ values, resulting in perverse outcomes such as the amplification of low-quality information engineered to reward hack and sensationalize. In this talk, I’ll introduce a pattern language for measuring and steering algorithmic systems towards users’ values. First, in a first series of lab and field experiments in the context of sharing misinformation on social media, I show that this can be mitigated by fostering alignment between sharing decisions and people's own stated values with attentional prompts. In a second project, I introduce a novel personalization architecture for measuring the expression of basic human values. I then apply it to a large-scale audit of Twitter/X’s feed algorithm to reveal how subtle value tensions in users' engagement behavior produces a fundamental misalignment between algorithmic amplification and user's self-stated values. To close, I will discuss ongoing work on re-conceptualizing AI systems to support human creativity and creative production.