  Widespread developments in automation have reduced the need for human input.
However, despite the increased power of machine learning, in many contexts
these programs make decisions that are problematic. Biases within data and
opaque models have amplified human prejudices, giving rise to such tools as
Amazon's (now defunct) experimental hiring algorithm, which was found to
consistently downgrade resumes when the word "women's" was added before an
activity. This article critically surveys the existing legal and technological
landscape surrounding algorithmic hiring. We argue that the negative impact of
hiring algorithms can be mitigated by greater transparency from the employers
to the public, which would enable civil advocate groups to hold employers
accountable, as well as allow the U.S. Department of Justice to litigate. Our
main contribution is a framework for automated hiring transparency, algorithmic
transparency reports, which employers using automated hiring software would be
required to publish by law. We also explain how existing regulations in
employment and trade secret law can be extended by the Equal Employment
Opportunity Commission and Congress to accommodate these reports.
