Location, location, location. It matters whether you’re looking for a house — or for humans.

The luxury six-hour roundtrip flight will include plush reclining seats, anti-glare windows for photography, and livestream-capable WiFi.

The HI-SEAS mock extraterrestrial base is part of an intensifying effort to get humans ready for living on other worlds, which could well be soon. NASA recently selected SpaceX as the company to build its Artemis lunar lander. At some point, our visits will have to become stays if human space exploration is to go beyond its 20th-century heyday.

In a recent paper, Bell argues that the principles of Mario Kart—especially the parts of it that make it so addictive and fun for players—can serve as a helpful guide to create more equitable social and economic programs that would better serve farmers in low-resource, rural regions of the developing world. That’s because, even when you’re doing horribly in Mario Kart—flying off the side of Rainbow Road, for example—the game is designed to keep you in the race.

“Farming is an awful thing to have to do if you don’t want to be a farmer,” Bell says. “You have to be an entrepreneur, you have to be an agronomist, put in a bunch of labor…and in so many parts of the world people are farmers because their parents are farmers and those are the assets and options they had.” This is a common story that Bell has come across many times during research trips to Pakistan, Bangladesh, Cambodia, Malawi, and other countries in southern Africa, and is what largely inspired him to focus his research on policies that could aid in development.

In his new paper, Bell argues that policies that directly provide assistance to farmers in the world’s poorest developing regions could help reduce poverty overall, while increasing sustainable and environmentally friendly practices. Bell says the idea is a lot like the way that Mario Kart gives players falling behind in the race the best power-ups, designed to bump them towards the front of the pack and keep them in the race. Meanwhile, faster players in the front don’t get these same boosts, and instead typically get weaker powers, such as banana peels to trip up a racer behind them or an ink splat to disrupt the other players’ screens. This boosting principle is called “rubber banding,” and it’s what keeps the game fun and interesting, Bell says, since there is always a chance for you to get ahead.

The point of the experiment was to show how easy it is to bias any artificial intelligence if you train it on biased data. The team wisely didn’t speculate about whether exposure to graphic content changes the way a human thinks. They’ve done other experiments in the same vein, too, using AI to write horror stories, create terrifying images, judge moral decisions, and even induce empathy. This kind of research is important. We should be asking the same questions of artificial intelligence as we do of any other technology because it is far too easy for unintended consequences to hurt the people the system wasn’t designed to see. Naturally, this is the basis of sci-fi: imagining possible futures and showing what could lead us there. Issac Asimov gave wrote the “Three Laws of Robotics” because he wanted to imagine what might happen if they were contravened.

Even though artificial intelligence isn’t a new field, we’re a long, long way from producing something that, as Gideon Lewis-Kraus wrote in The New York Times Magazine, can “demonstrate a facility with the implicit, the interpretive.” But it still hasn’t undergone the kind of reckoning that causes a discipline to grow up. Physics, you recall, gave us the atom bomb, and every person who becomes a physicist knows they might be called on to help create something that could fundamentally alter the world. Computer scientists are beginning to realize this, too. At Google this year, 5,000 employees protested and a host of employees resigned from the company because of its involvement with Project Maven, a Pentagon initiative that uses machine learning to improve the accuracy of drone strikes.

Norman is just a thought experiment, but the questions it raises about machine learning algorithms making judgments and decisions based on biased data are urgent and necessary. Those systems, for example, are already used in credit underwriting, deciding whether or not loans are worth guaranteeing. What if an algorithm decides you shouldn’t buy a house or a car? To whom do you appeal? What if you’re not white and a piece of software predicts you’ll commit a crime because of that? There are many, many open questions. Norman’s role is to help us figure out their answers.