IEEE Technology and Society Magazine - September 2022 - 56

Technological Stewardship and Responsible Innovation
Three key lessons emerge from Ewert underscoring
the importance of deep consideration of the
ethical concerns posed by the implementation of AI
in the criminal justice system. First, data-driven decision-making,
an early form of AI, has been in use by
justice systems around the world in the form of risk
assessment tools. Second, Canadian law requires
lower courts to prioritize individual factors and
appreciate systemic constraints affecting individuals
who appear before the Court, especially Canada's
Aboriginal population. Justice system participants
should also apply this reasoning to Black Canadians
and other minority groups. Third, despite these directives
in law and the recognized flaws in risk assessment
tools, AI appears to be the next frontier of risk
assessment tools in the criminal justice system.
The use of the basic level of data-driven decision-making
via risk assessment tools continues in
our criminal justice system, which should create
pause for ethical concerns for the future of AI and
algorithmic decision-making. Our justice system has
relied upon flawed risk assessment tools for decades,
proffering their results to courts as reliable and accurate.
In Ewert, the Supreme Court emphasized that
" discrimination experienced by Indigenous persons,
whether as a result of overtly racist attitudes or culturally
inappropriate practices, extends to all parts
of the criminal justice system including the prison
system " [3, ¶ 57], [27, ¶ 199].
Is the crImInal justice system ready for more
advanced forms of AI and machine learning? Will
advanced tools such as facial recognition or prediction
programs to assess the risk of bail or sentencing
be used in a " constellation of factors, " all the while,
slowly and steadily continuing to perpetuate the systemic
violence that is inherent in the criminal justice
system? Perhaps, the way forward in what seems to
be the inevitable adoption of AI and machine learning
in the criminal justice system is a concurrent
understanding of the systemic discrimination the
data informing our tools reinforces, a commitment
to change and ensuring better data, and a deep
understanding of how individual and systemic factors
have traditionally informed Canadian common
law. To echo the Supreme Court in Barton, " when
it comes to truth and reconciliation from a criminal
justice system perspective, much-needed work
remains to be done " [27, ¶ 199].
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56
 References
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[2] Ewert v. Canada, FC 1093, 2015.
[3] Ewert v. Canada, 2 S.C.R. 165, 2018.
[4] Law Commission of Ontario. The Rise and Fall of
AI and Algorithms in American Criminal Justice:
Lessons for Canada. Accessed: Oct. 28, 2020.
[Online]. Available: https://www.lco-cdo.org/wpcontent/uploads/2020/10/Criminal-AI-Paper-FinalOct-28-2020.pdf
[5]
Canada v. Ewert, FCA 203, 2016.
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[17] R v. Ipeelee, SCC 13, 2012.
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[20] R v. Lacasse, SCC 64, 2015.
[21] R. Benjamin, Race After Technology: Abolitionist Tools
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[22] H. Magee, " The criminal character: A critique of
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[24] C. C. Hurducas et al., " Violence risk assessment tools:
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