IEEE Technology and Society Magazine - June 2019 - 41

he adoption of data-driven organizational management - which includes big data, machine learning,
and artificial intelligence (AI) techniques - is growing rapidly across
all sectors of the knowledge economy. There is little doubt that the
collection, dissemination, analysis, and use of data
in government policy formation, strategic planning,
decision execution, and the daily performance of duties can improve the functioning of government and
the performance of public services. This is as true for
law enforcement as any other government service.
Significant concerns have been raised, however,
around the use of data-driven algorithms in policing,
law enforcement, and judicial proceedings. This
includes predictive policing - the use of historic crime
data to identify individuals or geographic areas with
elevated risks for future crimes, in order to target them
for increased policing. Predictive policing has been
controversial for multiple reasons, including questions
of prejudice and precrime1 and effectively treating people as guilty of (future) crimes for acts they have not
yet committed and may never commit. This central
controversy over prejudice and precrime is amplified
and exacerbated by concerns over the implicit biases
contained in historic data sets, and the obvious implications for racial, gendered, ethnic, religious, class,
age, disability, and other forms of discriminatory
policing, as well as how the use of predictive information systems shapes the psychology and behavior of
police officers.
As more bureaucratic processes are automated,
there are growing concerns over the fairness, accountability, and transparency of the algorithms used to
make consequential decisions that determine peoples'
life opportunities and rights. Less discussed are the
ways in which the introduction of data-centric processes
and data-driven management have significant consequences on the techno-social and spatio-temporal structure of organizations [1], as well as on the priorities of
organization management, the nature of labor, and the
quality of results [2]. Such is the nature of contemporary
technocratic governance [3]. Yet neither the increasing
collection and reliance on data, nor specific socio-technical and spatio-temporal organization of governmental
institutions is determined by the technology alone, nor
by the utility of data. Nor is the kind of analysis performed on that data, or the specific problems to which
the data is addressed, pre-determined or "natural" in

1
"Precrime" is a science fiction concept that first appeared in the writings
of Philip K. Dick in a novel [19] that was later turned into a major Hollywood
movie [20].

JUNE 2019

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any meaningful sense. Rather, there are myriad social,
institutional, and individual values that go into the decisions of which data to collect, when and where to collect it, how to encode it, how to assemble it in
databases, how to interpret it, and how to use it to
address social, institutional, and individual concerns. It
is those values which are the primary concern of ethics
in information systems design.
This article outlines a new ethical approach that balances the promising benefits of AI with the realities of
how information technologies and AI algorithms are
actually adopted, applied, and used. It proposes that AI
ethics should be driven by a substantive and systemic
Ethics of Care, rather than by narrow Models of Threat
based on utilitarian risk and threat models. While it
focuses on law enforcement policies and policing practices, it hopes to contribute to the broader discussion
over the ethical application of AI technologies in government policy-making and the delivery of public and commercial services more generally. The paper concludes
that while data-driven AI techniques could have many
socially beneficial applications, actually realizing those
benefits requires careful consideration of how systems
are embedded in, and shape, existing practices, beyond
questions of de-biasing data. Absent such consideration,
most applications are likely to have unjust, prejudicial,
and discriminatory consequences. This conclusion supports a proposed Ethics of Care in the application of AI,
which demands moral attention to those who may be
negatively impacted by the use of technology.

Recent Excitement about AI
There is a recent and widespread excitement about the
application of artificial intelligence to nearly every
aspect of society - from commerce to government. AI,
as a scientific research field, has long sought to develop computer programs to perform tasks that were previously thought to require human intelligence. This
somewhat abstract and conditional definition has given
rise to a wide array of computational techniques, from
logical inference to statistical machine learning, that
enable computers to process large and complex datasets and quickly provide useful information. Whether
through traversing long chains of inference or sifting
through vast amounts of data to find patterns, AI aims
to provide logically sound and evidence-based insights
into datasets. Insofar as these datasets accurately represent phenomena in the world, such AI techniques can
potentially provide useful tools for analyzing that data
and choosing intelligent actions in response to that
analysis, all with far less human labor and effort. This is
the traditional approach of AI, or what we might consider artificial specialized intelligence. This type of AI is
essentially about creating a customized piece of

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IEEE Technology and Society Magazine - June 2019

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