Archive for Technology & Society
March 1, 2017 at 12:00 pm · Filed under artificial intelligence, jb exploits, media impact, online culture, social media and tagged: filter bubble, IEEE, Ramona Pringle, recommendation engines, Technology & Society
An article of mine on the “Technologies of Taste” has just come out in Technology & Society, a publication of the Institute of Electrical and Electronics Engineers (IEEE). It’s a fascinating special issue exploring the “Unintended Consequences of Technology.” As the guest editor, Ramona Pringle explained it to me that the focus wasn’t on “the dark side” of tech, but rather the complicated nature of our increasingly connected lives.
The call for papers, however, emphasized the danger of not carefully examining our relationship to new technology:
With all great innovation comes responsibility; and with the exponential growth of technology, the window within which we can examine the ethics and consequences of our adoption of new technologies becomes increasingly narrow. Instead of fear mongering, how do we adjust our course, as a society, before it is too late?
My piece explores the role that recommendation systems play in our online pursuits of knowledge and pleasure. How is our personal taste affected by finely-tuned commercial algorithms that are optimized to sell us products and monetize our attention? While Eli Pariser and others have argued that these systems place us in “filter bubbles” that insulate us from new ideas, I argue that companies like Google, Amazon and Netflix have strong commercial incentives to develop recommendation systems that broaden their customers’ horizons rather than limiting them, effectively bursting filter bubbles rather than reinforcing them.
This couldn’t be a more timely argument considering that concerns about filter bubbles have grown exponentially during the last presidential election cycle. What complicates the debate about filter bubbles is that each site — whether it’s primarily an ecommerce, social media, search or content platform — has very different goals in mind and different proprietary algorithms in place to achieve them. I hope this article triggers a more thoughtful conversation when people claim that ideological insularity is the obvious outcome of filtering and recommendation technology.