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71 Admin. L. Rev. 1 (2019)
Transparency and Algorithmic Governance

handle is hein.journals/admin71 and id is 13 raw text is: 









                          ARTICLES



                     TRANSPARENCY AND
            ALGORITHMIC GOVERNANCE


                    GARY COGLIANESE* & DAVID LEHR**
   Machine-learning algorithms are improving and automating importantfunctions in med-
icine, transportation, and business. Government officials have also started to take notice of
the accuracy and speed that such algorithms provide, increasingly relying on them to aid
with consequential public-sector functions, including tax administration, regulatoy over-
sht, and benefits administration. Despite machine-learning algorithms' superior predictive
power over conventional analytic tools, algorithmic forecasts are dicult to understand and
explain. Machine learning's black box nature has thus raised concern: Can algorithmic
governance be squared with legal principles ofgovernmental transparency? We analyze this
question and conclude that machine-learning algorithms' relative inscrutabiliy does not pose
a legal barrier to their responsible use by governmental authorities. We distinguish between
principles of 'fishbowl transparency and reasoned transparency,  explaining how both
are implicated by algorithmic governance but also showing that neither conception compels
anything close to total transparency. Although machine learning's black-box features dis-
tinctively implicate notions of reasoned transparency, legal demands for reason-giving can
be satisfied by explaining an algorithm's purpose, design, and basic functioning. Further-
more, new technical advances will only make machine-learning algorithms increasingly more



* Edward B. Shils Professor of Law and Political Science and Director of the Penn Program
on Regulation, University of Pennsylvania Law School.
** Research Affiliate, Penn Program on Regulation;J.D. Candidate, 2020, Yale Law School.
We thank Lavi Ben Dor, Harrison Gunn, AlexandraJohnson, andJessica Zuo for their helpful
research and editorial assistance, as well as Alissa Kalinowski, Caroline Raschbaum, and their
colleagues on this journal for their careful editorial guidance. We are grateful for constructive
substantive comments provided by Stuart Benjamin, Richard Berk, Harrison Gunn, Richard
Pierce, and Arti Rai. We also acknowledge appreciatively a spirited discussion at Duke Law
School's 2018 conference on artificial intelligence in the administrative state, which sparked
our interest in developing this extended analysis of transparency issues.

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