Tailored AI surpasses humans in creating and implementing Fiscal Redistribution- sequencing means the aff replaces it.

(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 systems aren't just good at solving and , 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 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 , 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. While AIs can produce , they can also arrive at when left to their own devices; human feedback can help to steer neural networks in a better direction. "In AI research, there is a growing realization that to build human-compatible systems, we need new research methods in which humans and agents interact, and an increased effort to learn values directly from humans to build value-aligned AI," the . In experiments involving thousands of human participants in total, the team's AI agent – called 'Democratic AI' – studied an investment exercise called the , in which players receive varying amounts of money, and can contribute their money to a public fund, and then draw a return from the fund corresponding to their level of investment. In a series of different game styles, wealth was redistributed to players via three traditional redistribution paradigms – strict egalitarian, libertarian, and liberal egalitarian – each of which rewards player investments differently. A fourth method was also tested, called the Human Centered Redistribution Mechanism (HCRM), developed using , using feedback data from both human players and virtual agents designed to imitate human behavior. Subsequent experiments showed that the HCRM system for paying out money in the game was more popular with players than any of the traditional redistribution standards, and also more popular than new redistribution systems designed by human referees who were incentivized to create popular systems by receiving small per-vote payments. "The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders, and successfully won the majority vote," . "We show that it is possible to harness for value alignment the same democratic tools for achieving consensus that are used in the wider human society to elect representatives, decide public policy or make legal judgements." It's worth noting that the researchers acknowledge their system raises a number of questions – chiefly, that value alignment in their AI revolves around democratic determinations, meaning the agent could actually exacerbate inequalities or biases in society (provided they are popular enough to be voted for by a majority of people).