The complex math of counterfactuals could help Spotify pick your next favorite song

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“Causal reasoning is critical for machine learning,” says Nailong Zhang, a software engineer at Meta. Meta is using causal inference in a machine-learning model that manages what number of and what sorts of notifications Instagram should send its users to maintain them coming back. 

Romila Pradhan, a knowledge scientist at Purdue University in Indiana, is using counterfactuals to make automated decision making more transparent. Organizations now use machine-learning models to decide on who gets credit, jobs, parole, even housing (and who doesn’t). Regulators have began to require organizations to elucidate the end result of a lot of these decisions to those affected by them. But reconstructing the steps made by a posh algorithm is tough. 

Pradhan thinks counterfactuals may also help. Let’s say a bank’s machine-learning model rejects your loan application and you ought to know why. One technique to answer that query is with counterfactuals. Provided that the applying was rejected within the actual world, wouldn’t it have been rejected in a fictional world by which your credit history was different? What about in the event you had a unique zip code, job, income, and so forth? Constructing the power to reply such questions into future loan approval programs, Pradhan says, would give banks a technique to offer customers reasons reasonably than simply a yes or no.    

Counterfactuals are vital since it’s how people take into consideration different outcomes, says Pradhan: “They’re a great technique to capture explanations.”

They also can help firms predict people’s behavior. Because counterfactuals make it possible to infer what might occur in a specific situation, not only on average, tech platforms can use it to pigeonhole individuals with more precision than ever. 

The identical logic that may disentangle the results of dirty water or lending decisions will be used to hone the impact of Spotify playlists, Instagram notifications, and ad targeting. If we play this song, will that user listen for longer? If we show this picture, will that person keep scrolling? “Firms want to grasp the way to give recommendations to specific users reasonably than the typical user,” says Gilligan-Lee.

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