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22 Stan. Tech. L. Rev. 242 (2019)
Developing Artificially Intelligent Justice

handle is hein.journals/stantlr22 and id is 243 raw text is: 

   Developing Artificially Intelligent Justice

          Richard M. Re & Alicia Solow-Niederman*

                     22 STAN. TECH. L. REV. 242 (2019)


    Artificial intelligence, orAI, promises to assist, modify, and replace human
decision-making, including in court. AI already supports many aspects of how
judges  decide cases, and the prospect  of robot judges suddenly seems
plausible-even   imminent.  This Article argues that AI adjudication  will
profoundly affect the adjudicatory values held by legal actors as well as the
public at large. The impact is likely to be greatest in areas, including criminal
justice and   appellate  decision-making, where   equitable justice, or
discretionary moral  judgment,  is frequently considered  paramount.   By
offering efficiency and at least an appearance of impartiality, AI adjudication
will both foster  and benefit from  a  turn toward   codified justice, an
adjudicatory paradigm  thatfavors standardization above discretion. Further,
AI adjudication will generate a range of concerns relating to its tendency to
make  the legal system more  incomprehensible, data-based, alienating, and
disillusioning. And potential responses, such as crafting a division of labor
between  human   and AI adjudicators, each pose their own challenges. The
single most promising response is for the government to play a greater role in
structuring the  emerging  market  for AI justice, but auspicious reform

      * Professor of Law, UCLA School of Law; 2017-2019 PULSE Fellow, UCLA School
of Law and 2019-2020 Law Clerk, U.S. District Court for the District of Columbia. Alicia
Solow-Niederman completed this work during her tenure as a PULSE Fellow, and the
arguments advanced here are made in her personal capacity. The authors, whose names
are listed alphabetically, are grateful to participants at the Yale Law School Information
Society Project's (Im)perfect Enforcement Conference, the UCLA School of Law AI in
Strategic Context Conference, and the UCLA Department of Political Science Media
Immersion and Forms of Life Workshop. Thanks also to Dan Bussel, Kiel Brennan-
Marquez, Margot Kaminski, Karen Levy, Martha Minow, Ted Parson, Joanna Schwartz,
John Tasioulas, and Eugene Volokh for thoughtful comments and helpful conversations,
as well as to Cooper Mayne for excellent research assistance. This research was partly
funded by a gift from the Open Philanthropy Project to the UCLA School of Law. Finally,
many thanks to the editors of the Stanford Technology Law Review.

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