Abstract
Algorithms play a key role in the functioning of
autonomous systems, and so concerns have
periodically been raised about the possibility of
algorithmic bias. However, debates in this area
have been hampered by different meanings and
uses of the term, “bias.” It is sometimes used as a
purely descriptive term, sometimes as a pejorative
term, and such variations can promote confusion
and hamper discussions about when and how to
respond to algorithmic bias. In this paper, we first
provide a taxonomy of different types and sources
of algorithmic bias, with a focus on their different
impacts on the proper functioning of autonomous
systems. We then use this taxonomy to distinguish
between algorithmic biases that are neutral or
unobjectionable, and those that are problematic in
some way and require a response. In some cases,
there are technological or algorithmic adjustments
that developers can use to compensate for
problematic bias. In other cases, however,
responses require adjustments by the agent,
whether human or autonomous system, who uses
the results of the algorithm. There is no “one size
fits all” solution to algorithmic bias