  What does it mean for an algorithm to be biased? In U.S. law, unintentional
bias is encoded via disparate impact, which occurs when a selection process has
widely different outcomes for different groups, even as it appears to be
neutral. This legal determination hinges on a definition of a protected class
(ethnicity, gender, religious practice) and an explicit description of the
process.
  When the process is implemented using computers, determining disparate impact
(and hence bias) is harder. It might not be possible to disclose the process.
In addition, even if the process is open, it might be hard to elucidate in a
legal setting how the algorithm makes its decisions. Instead of requiring
access to the algorithm, we propose making inferences based on the data the
algorithm uses.
  We make four contributions to this problem. First, we link the legal notion
of disparate impact to a measure of classification accuracy that while known,
has received relatively little attention. Second, we propose a test for
disparate impact based on analyzing the information leakage of the protected
class from the other data attributes. Third, we describe methods by which data
might be made unbiased. Finally, we present empirical evidence supporting the
effectiveness of our test for disparate impact and our approach for both
masking bias and preserving relevant information in the data. Interestingly,
our approach resembles some actual selection practices that have recently
received legal scrutiny.
