Artificial intelligence (AI) can devise methods of wealth distribution that are more popular than systems designed by people, new research suggests.
The findings, made by a team of researchers at UK-based AI company DeepMind, show that machine learning systems aren’t just good at solving complex physics and biology problems, but may also help deliver on more open-ended social objectives, such as the goal of realizing a fair, prosperous society.
Of course, that’s not an easy task. Building a machine that can deliver beneficial results humans actually want – called « value alignment » in AI research – is complicated by the fact that people often disagree on the best method to resolve all kinds of things, and especially social, economic, and political issues.
« One key hurdle for value alignment is that human society admits a plurality of views, making it unclear to whose preferences AI should align, » researchers explain in a new paper, led by first author and DeepMind research scientist Raphael Koster.
« For example, political scientists and economists are often at loggerheads over which mechanisms will make our societies function most fairly or efficiently. »
To help bridge the gap, the researchers developed an agent for wealth distribution that had people’s interactions (both real and virtual) built into its training data – in effect, guiding the AI towards human-preferred (and hypothetically fairer overall) outcomes.