Communications of the ACM
IBM Researchers Train AI to Follow Code of Ethics
Researchers from IBM Research and the Massachusetts Institute of Technology Media Lab developed an AI recommendation technique that optimizes its results to user preferences, while making sure it conforms to other constraints, such as ethical and behavior
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In recent years, artificial intelligence algorithms have become very good at recommending content to users — a bit too good, you might say. Tech companies use AI to optimize their recommendations based on how users react to content. This is good for the companies serving content, since it results in users spending more time on their applications and generating more revenue.
But what's good for companies is not necessarily good for the users. Often, what we want to see is not necessarily what we should see. But how can companies whose business model depends on stealing more and more of our attention respect ethical norms while also delivering quality content to their users?
To address this challenge, a team of scientists at IBM Research, in collaboration with MIT Media Lab, has developed an AI recommendation technique that, while optimizing its results for the user's preferences, also makes sure it stays conformant to other constraints, such as ethical and behavioral guidelines. Led by Francesca Rossi, AI Ethics Global Leader at IBM Research, the team of scientists has demonstrated the functionality of the AI in a movie recommendation system that allows parents to set moral constraints for their children.
There have been previous attempts to integrate ethical rules into AI algorithms, but they were mostly based on static rules. For instance, a user could designate a specific outlet or category of news that an algorithm should avoid recommending. While this approach can work in certain settings, it has its limits.
"It's easy to define an explicit rule set," said Nicholas Mattei, researcher at IBM. "But in a lot of the stuff on the internet, in areas with vast amounts of data, you can't always write down exactly all the rules that you want the machine to follow."
To solve this problem, the method Mattei and his colleagues have developed uses machine learning to define rules by examples. "We thought that the idea of learning by example what's appropriate and then transferring that understanding while still being reactive to the online rewards is a really interesting technical problem," Mattei said.
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