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8 Cambridge L. Rev. 1 (2023)
The Guilty (Silicon) Mind: Blameworthiness and Liability in Human-Machine Teaming

handle is hein.journals/cambrilv8 and id is 5 raw text is: 

The Guilty (Silicon) Mind    1

Cambridge Law Review (2023) Vol 8, Issue 1, 1-24

The Guilty (Silicon) Mind: Blameworthiness

   and Liability in Human-Machine Teaming



As science pushes the boundaries of the development of artificial intelligence (AI),
the progress has caused scholars and policymakers alike to question the legality of
utilising Al in various human endeavours. Debate has raged in international schol-
arship about the legitimacy of applying Al to weapon systems to form lethal
autonomous  weapon  systems (LAWS). Yet the legality of applying or utilising Al
is questionable even when Al is applied to a non-weaponised autonomous system:
how does one hold a machine accountable for a crime? What about a tort? Can an
artificial agent understand the moral and ethical content of its instructions? These
are thorny questions, and in many cases, these questions have been answered in
the negative, as artificial entities lack any contingent moral agency. What then oc-
curs if the AI is not alone, but linked with or overseen by a human being, with
their own moral and ethical understandings and obligations? Who is responsible
for any malfeasance that may be committed? Does the human bear the legal risks
of unethical or immoral decisions of an AI? These are some of the questions with
which this manuscript seeks to engage.

Keywords: human-machine teaming, liability, criminal law, civil law, military

                           I. INTRODUCTION

Automation  has been a key result of mankind's technological development over
the last two centuries. Rather than a reliance on manual labour, we have developed
mechanised  tools which replace our efforts with streamlined and optimised acts.

* Senior Research Fellow, Law and Future of War Research Group, The University of Queensland; JD,
** Senior Research Fellow, School of Engineering, Australian National University; BAeroEng, PhD.
The research for this paper received funding from the Australian Government through Trusted Autono-
mous Systems, a Defence Cooperative Research Centre funded through the Next Generation
Technologies Fund.

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