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Algorithms Need Managers, Too
Michael Luca, Jon Kleinberg & Sendhil Mullainathan
Executive SummaryAlgorithms are powerful predictive tools, but they can run amok when not applied properly.
Consider what often happens with social media sites. Today many use algorithms to decide
which ads and links to show users. But when these algorithms focus too narrowly on
maximizing click-throughs, sites quickly become choked with low-quality content. While clicks
rise, customer satisfaction plummets.
The glitches, say the authors, are not in the algorithms but in the way we interact with them.
Managers need to recognize their two major limitations: First, they’re completely literal;
algorithms do exactly what they’re told and disregard every other consideration. While a
human would have understood that the sites’ designers wanted to maximize quality as
measured by clicks, the algorithms maximized clicks at the expense of quality. Second,
algorithms are black boxes. Though they can predict the future with great accuracy, they
won’t say what will cause an event or why. They’ll tell you which magazine articles are likely
to be shared on Twitter without explaining what motivates people to tweet about them, for
To avoid missteps, you need to be explicit about all your goals—hard and soft—when
formulating your algorithms. You also must consider the long-term implications of the data
the algorithms incorporate to make sure they’re not focusing near-sightedly on short-term
outcomes. And choose the right data inputs, being sure to gather a wide breadth of
information from a diversity of sources.
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