For recommendation algorithms to examine these relationships, they need a lot of data. Therefore many providers, such as Netflix, Amazon and Spotify, ask users to rate content.

Nevertheless, between April 2011 and June 2013, the algorithm scrutinized the patterns of more than 312,000 patients in Banner Health’s 24 hospitals. The results?

Hitting Dislike, the most visible way to provide negative feedback, stops only 12% of bad recommendations; Not interested stops just 11%. YouTube advertises both options as ways to tune its algorithm.

How Recommendation Algorithms Limit Searches Results. Internet-based companies measure a person’s satisfaction with a site or service by their attention to the service.

As the New York Times’ Rabbit Hole podcast explores, YouTube’s recommendation algorithms can drive viewers to increasingly extreme content, potentially leading to online radicalization.

Lawmakers have frequently criticized social media giants for using recommendation algorithms to boost user engagement, but so far, there’s been little legislative action to curb their use.

Data-based decision-making, including social media recommendation algorithms or machine learning systems, often lives in proverbial black boxes.

Algorithms may entrench our biases, homogenize and flatten culture, and exploit and suppress the vulnerable and marginalized. But these aren’t completely inscrutable systems or inevitable ...

Meta is publishing new material to explain how its recommendation engine works across different products. Along with that, it is also rolling out a new option called “Why am I seeing this ...