New tool improves fairness of online search rankings

Date:
August 18, 2020
Source:
Cornell University
Summary:
Researchers introduce a tool they've developed to improve the
fairness of online rankings without sacrificing their usefulness
or relevance.



FULL STORY ==========================================================================
When you search for something on the internet, do you scroll through
page after page of suggestions -- or pick from the first few choices?

========================================================================== Because most people choose from the tops of these lists, they rarely
see the vast majority of the options, creating a potential for bias in everything from hiring to media exposure to e-commerce.

In a new paper, Cornell University researchers introduce a tool they've developed to improve the fairness of online rankings without sacrificing
their usefulness or relevance.

"If you could examine all your choices equally and then decide what
to pick, that may be considered ideal. But since we can't do that,
rankings become a crucial interface to navigate these choices," said
computer science doctoral student Ashudeep Singh, co-first author of "Controlling Fairness and Bias in Dynamic Learning-to-Rank," which won
the Best Paper Award at the Association for Computing Machinery SIGIR Conference on Research and Development in Information Retrieval.

"For example, many YouTubers will post videos of the same recipe, but
some of them get seen way more than others, even though they might be
very similar," Singh said. "And this happens because of the way search
results are presented to us. We generally go down the ranking linearly
and our attention drops off fast." The researchers' method, called
FairCo, gives roughly equal exposure to equally relevant choices and
avoids preferential treatment for items that are already high on the
list. This can correct the unfairness inherent in existing algorithms,
which can exacerbate inequality and political polarization, and curtail personal choice.

"What ranking systems do is they allocate exposure. So how do we make
sure that everybody receives their fair share of exposure?" said Thorsten Joachims, professor of computer science and information science, and
the paper's senior author. "What constitutes fairness is probably very different in, say, an e- commerce system and a system that ranks resumes
for a job opening. We came up with computational tools that let you
specify fairness criteria, as well as the algorithm that will provably
enforce them." Algorithms seek the most relevant items to searchers,
but because the vast majority of people choose one of the first few items
in a list, small differences in relevance can lead to huge discrepancies
in exposure. For example, if 51% of the readers of a news publication
prefer opinion pieces that skew conservative, and 49% prefer essays that
are more liberal, all of the top stories highlighted on the home page
could conceivably lean conservative, according to the paper.

"When small differences in relevance lead to one side being amplified,
that often causes polarization, where some people tend to dominate the conversation and other opinions get dropped without their fair share of attention," Joachims said. "You might want to use it in an e-commerce
system to make sure that if you're producing a product that 30% of people
like, you're getting a certain amount of exposure based on that. Or if
you have a resume database, you could formulate safeguards to make sure
it's not discriminating by race or gender." The research was partly
supported by the National Science Foundation and by Workday.


========================================================================== Story Source: Materials provided by Cornell_University. Original written
by Melanie Lefkowitz. Note: Content may be edited for style and length.


==========================================================================


Link to news story: https://www.sciencedaily.com/releases/2020/08/200818114947.htm

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