They promised us the robots wouldn't attack… In what seems like dialogue lifted straight from the pages of a post-apocalyptic science fiction novel, researchers from the University of Massachusetts Amherst and Stanford claim they've developed an algorithmic framework that guarantees AI won't misbehave. The framework uses'Seldonian' algorithms, named for the protagonist of Isaac Asimov's "Foundation" series, a continuation of the fictional universe where the author's "Laws of Robotics" first appeared. According to the team's research, the Seldonian architecture allows developers to define their own operating conditions in order to prevent systems from crossing certain thresholds while training or optimizing. In essence, this should allow developers to keep AI systems from harming or discriminating against humans. Deep learning systems power everything from facial recognition to stock market predictions.

Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors. Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints. "We call algorithms created with our new framework'Seldonian' after Asimov's character Hari Seldon," Thomas explains.

Artificial intelligence, it seems, can figure out how to do just about anything. It can simulate the Universe, learn to solve a Rubik's Cube with just one hand, and even find ghosts hidden in our past. All these kinds of advancements are meant to be for our own good. In recent times, algorithmic systems that already affect people's lives have demonstrated alarming levels of bias in their operation, doing things like predicting criminality along racial lines and determining credit limits based on gender. Against this backdrop, how can scientists ensure that advanced thinking systems can be fair, or even safe?

Machine learning, a process in which artificial intelligence teaches itself to perform complex tasks, has boundless applications. But the risks are alarming. AI, for instance, could discriminate against hiring black people based on past trends when discrimination against them was rife. So, in order to avoid such undesirable behaviour, a team of computer scientists at Stanford University has developed a framework dubbed "Seldonian algorithms" after a character in the science-fiction novels of Isaac Asimov. Seldonian algorithms can easily be tweaked by end users--who may not be coding wizards--to pre-empt potential foul-ups.

Seventy years ago, science fiction writer Isaac Asimov imagined a world where robots would serve humans in countless ways, and he equipped them with built-in safeguards now known as Asimov's Three Laws of Robotics, to prevent them, among other goals, from ever harming a person. Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors. Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints.

IMAGE: Philip Thomas at UMass Amherst, with colleagues there and at Stanford, says they say they hope that machine learning researchers will go on to develop new and more sophisticated... view more Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors. Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints. "We call algorithms created with our new framework'Seldonian' after Asimov's character Hari Seldon," Thomas explains.