Professor James Zou, says that as algorithms compete for clicks and the associated user data, they become more specialized for subpopulations that gravitate to their sites. This can have serious implications for both companies and consumers.
This is described in a paper "Competing AI: How does competition feedback affect machine learning?", written by Antonio Ginart (EE PhD candidate), Eva Zhang, and professor James Zou.
James' team recognized that there's a feedback dynamic at play if companies' machine learning algorithms are competing for users or customers and at the same time using customer data to train their model. "By winning customers, they're getting a new set of data from those customers, and then by updating their models on this new set of data, they're actually then changing the model and biasing it toward the new customers they've won over," says Antonio Ginart.
In terms of next steps, the team is looking at the effect that buying datasets (rather than collecting data only from customers) might have on algorithmic competition. James is also interested in identifying some prescriptive solutions that his team can recommend to policymakers or individual companies. "What do we do to reduce these kinds of biases now that we have identified the problem?" he says.
"This is still very new and quite cutting-edge work," James says. "I hope this paper sparks researchers to study competition between AI algorithms, as well as the social impact of that competition."
Excerpted from "When Algorithms Compete, Who Wins?"
Stanford HAI's mission is to advance AI research, education, policy and practice to improve the human condition.