Addressing Information Overload from Polarization

Although mostly we had too little data to address a problem, the idea of finding data, determining its value in context and then prepping for sub tasks involved, could lead to too much.  This article excerpt intrigued me.  Polarization did occur, people wanted to use familiar, easy to use and common data that they had in their control.  How far is this beyond confirmation bias?  To some degree we used a form of risk analysis to gauge the danger.   But a more general, up front method could be useful.   Though I think general summarization would also introduce risk.    Will take a look at the original publication. 

Algorithms Could Reduce Polarization From Information Overload
Rensselaer Polytechnic Institute
July 30, 2020

Computer scientists at Rensselaer Polytechnic Institute, the University of Illinois at Urbana Champaign, the University of California, Los Angeles, and the University of California, San Diego are calling for new algorithms that prioritize providing a broader view of available information online. They suggest awareness of this could avoid the polarization often caused by information overload. Said Rensselaer's Boleslaw Szymanski, "[T]he attention span of human beings is not prepared for hundreds of millions of authors. We don't know what to read, and since we cannot select everything, we simply go back to the familiar, to works that represent our own beliefs." The scientists propose a technique in which algorithms shift from "extractive summarization," which focuses on content that was consumed in the past, to "abstractive summarization," which expands the share of available thought that can be digested. .. "