{"id":14152,"date":"2025-04-28T22:00:00","date_gmt":"2025-04-28T22:00:00","guid":{"rendered":"https:\/\/modernsciences.org\/staging\/4414\/?p=14152"},"modified":"2025-04-16T06:28:36","modified_gmt":"2025-04-16T06:28:36","slug":"ai-alignment-measurement-human-goals-misalignment-research-april-2025","status":"publish","type":"post","link":"https:\/\/modernsciences.org\/staging\/4414\/ai-alignment-measurement-human-goals-misalignment-research-april-2025\/","title":{"rendered":"Getting AIs working toward human goals \u2212 study shows how to measure misalignment"},"content":{"rendered":"\n\n\n
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\n Self-driving cars are only one example where it\u2019s tricky but critical to align AI and human goals.\n AP Photo\/Michael Liedtke<\/a><\/span>\n <\/figcaption>\n <\/figure>\n\n Aidan Kierans<\/a>, University of Connecticut<\/a><\/em><\/span>\n\n

Ideally, artificial intelligence agents aim to help humans, but what does that mean when humans want conflicting things? My colleagues and I<\/a> have come up with a way to measure the alignment of the goals of a group of humans and AI agents.<\/p>\n\n

The alignment problem<\/a> \u2013 making sure that AI systems act according to human values \u2013 has become more urgent as AI capabilities grow exponentially<\/a>. But aligning AI to humanity seems impossible in the real world because everyone has their own priorities. For example, a pedestrian might want a self-driving car to slam on the brakes if an accident seems likely, but a passenger in the car might prefer to swerve.<\/p>\n\n

By looking at examples like this, we developed a score for misalignment<\/a> based on three key factors: the humans and AI agents involved, their specific goals for different issues, and how important each issue is to them. Our model of misalignment is based on a simple insight: A group of humans and AI agents are most aligned when the group\u2019s goals are most compatible.<\/p>\n\n

In simulations, we found that misalignment peaks when goals are evenly distributed among agents. This makes sense \u2013 if everyone wants something different, conflict is highest. When most agents share the same goal, misalignment drops.<\/p>\n\n

Why it matters<\/h2>\n\n

Most AI safety research treats alignment as an all-or-nothing property. Our framework shows it\u2019s more complex. The same AI can be aligned with humans in one context but misaligned in another.<\/p>\n\n

This matters because it helps AI developers be more precise about what they mean by aligned AI. Instead of vague goals, such as align with human values, researchers and developers can talk about specific contexts and roles for AI more clearly. For example, an AI recommender system \u2013 those \u201cyou might like\u201d product suggestions \u2013 that entices someone to make an unnecessary purchase could be aligned with the retailer\u2019s goal of increasing sales but misaligned with the customer\u2019s goal of living within his means.<\/p>\n\n

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