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algorithmic transparency
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Algorithmic
transparency is openness about the purpose, structure and
underlying actions of the algorithms used to search for, process and deliver
information. An algorithm is a set of steps that a computer program follows
in order to make a decision about a particular course of action.
The question of whether or not algorithms
that affect the general public should be made transparent is controversial.
Take, for example, a program used to determine credit scores. Should someone
be given a lower credit rating than they think they deserve, they have the
right to appeal the score, but not the right to demand that algorithms used
to determine the poor score be made public. This is because the company that
determined the person's credit score also has rights -- in this case, the
right to protect their intellectual property (IP).
A common method used to provide
transparency and ensure algorithmic accountability is the use of third party
audits. This approach is known as qualified
transparency. After complaints were made to the Federal Trade
Commission about the search giant Google, for example, watch-dog algorithms
created by FTC staffers found that Google's search algorithms generally
caused its own services to appear ahead of others in search results. To
provide transparency, the criteria used in the evaluation, as well as the
results, were publicly released and explained. Although the FTC decided
Google's actions were not anti-competitive in nature, the negative publicity
the investigation created inspired Google to make changes.
Decisions made by algorithms can be opaque
because of technical and social reasons, in addition to being made purposely
opaque to protect intellectual property. For example, the algorithms may be
too complex to explain or efforts to explain the algorithms might require the
use of data that violates a country's privacy regulations. Regardless of the
reasons, governments, corporations and private organizations throughout the
world are exploring ways to address the issue of algorithmic best practices
and accountability and provide the general public with as much transparency
as possible to build trust.
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Good Netiquette
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