  Decades of research suggest that information exchange in groups and
organizations can reliably improve judgment accuracy in tasks such as financial
forecasting, market research, and medical decision-making. However, we show
that improving the accuracy of numeric estimates does not necessarily improve
the accuracy of decisions. For binary choice judgments, also known as
classification tasks--e.g. yes/no or build/buy decisions--social influence is
most likely to grow the majority vote share, regardless of the accuracy of that
opinion. As a result, initially inaccurate groups become increasingly
inaccurate after information exchange even as they signal stronger support. We
term this dynamic the "crowd classification problem." Using both a novel
dataset as well as a reanalysis of three previous datasets, we study this
process in two types of information exchange: (1) when people share votes only,
and (2) when people form and exchange numeric estimates prior to voting.
Surprisingly, when people exchange numeric estimates prior to voting, the
binary choice vote can become less accurate even as the average numeric
estimate becomes more accurate. Our findings recommend against voting as a form
of decision-making when groups are optimizing for accuracy. For those cases
where voting is required, we discuss strategies for managing communication to
avoid the crowd classification problem. We close with a discussion of how our
results contribute to a broader contingency theory of collective intelligence.
