Do you study the phenomenon of information bubbles at Google? Let's say, a German user just happens to watch some right-wing populist video claiming that we need to stop Merkel's refugee politics. The next day the user might receive plenty of recommendations in their feed that confirm the message in the first video. They happen to stumble upon a video of some party convention by an uprising German populist party, and everything makes sense now! Video by video the user gets dragged into a right-wing ideology.
That is an information bubble. The algorithm cannot detect low quality or populism, neither can it recommend opposite standpoints, and at the end of the day it has a real effect on a country's politics and the well-being of many people.
Do you have means of quantifying such effects? What are possible countermeasures?
If you cannot talk about that, then this would be my feedback: Perhaps you could train a language model to find opposing views in video titles and tags and then diversify the video recommendations based on that.
That is an information bubble. The algorithm cannot detect low quality or populism, neither can it recommend opposite standpoints, and at the end of the day it has a real effect on a country's politics and the well-being of many people.
Do you have means of quantifying such effects? What are possible countermeasures?
If you cannot talk about that, then this would be my feedback: Perhaps you could train a language model to find opposing views in video titles and tags and then diversify the video recommendations based on that.