Distortion

Category: When Analytics Does Not Work

People can be gradually led into supporting more and more extreme views and this is a well-known effect of some recommendation engines. And it's a well-known phenomenon whereby people who have taken a position on an issue will, when questioned, entrench their views and interpret evidence in such a way as to favour that position. (Mercier & Sperber, 2017, p. 121)

An oft-made critique of algorithms like the YouTube recommendation system is that it tends to lead from relatively benign content to increasingly negative, polarizing or radical content. "It seems as if you are never 'hard core' enough for YouTube's recommendation algorithm. It promotes, recommends and disseminates videos in a manner that appears to constantly up the stakes." As an advertising engine, YouTube needs to maximize views, which leads to increasingly radical content. "Negative and novel information grab[s] our attention as human beings and [] cause[s] us to want to share that information with others—we're attentive to novel threats and especially attentive to negative threats." (Chesney and Citron, 2018:1753; Meyer, 2018)

Radicalization seems clearly to be undesirable and to pose an ethical problem for recommendation algorithms. But what if we misled them about the position they actually took in a more positive way? In a recent study, "By making people believe that they wrote down different responses moments earlier, we were able to make them endorse and express less polarized political views" (Strandberg, et.al., 2020). That sounds great, but is it ethical?

Examples and Articles

Tay, Microsoft's AI chatbot, gets a crash course in racism from Twitter
"Attempt to engage millennials with artificial intelligence backfires hours after launch, with TayTweets account citing Hitler and supporting Donald Trump" The Guardian. Elle Hunt, Thu 24 Mar 2016 Direct Link

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