a-nourishing-network/content/Essays/Re-Centralization-of-AI.md

Title: Re-Centralization of AI focusing on Social Justice
Author: Adnan Hadzi, Denis Roio

# RE - CENTRALIZATION OF AI FOCUSING ON SOCIAL JUSTICE

In order to lay the foundations for a discussion around
the argument that the adoption of artificial
intelligence (AI) technologies benefits the powerful
few, 1 focussing on their own existential concerns, 2 we
decided to narrow down our analysis of the argument
to social justic (i.e. restorative justice). This paper
signifies an edited version of Adnan Hadzi’s text on
Social Justice and Artificial Intelligence, 3 exploring the
notion of humanised artificial intelligence 4 in order to
discuss potential challenges society might face in the
future. The paper does not discuss current forms and
applications of artificial intelligence, as, so far, there
is no AI technology, which is self-conscious and self-
aware, being able to deal with emotional and social
intelligence. 5 It is a discussion around AI as a speculative
hypothetical entity. One could then ask, if such a speculative
self-conscious hardware/software system were created, at what
point could one talk of personhood? And what criteria could
there be in order to say an AI system was capable of
committing AI crimes?
Concerning what constitutes AI crimes the paper uses the
criteria given in Thomas King et al.’s paper Artificial
Intelligence Crime: An Interdisciplinary Analysis of Foreseeable
Threats and Solutions, 6 where King et al. coin the term “AI
crime”. We discuss the construction of the legal system through
the lens of political involvement of what one may want to
consider to be ‘powerful elites’ 7 . In doing so we will be
demonstrating that it is difficult to prove that the adoption of AI
technologies is undertaken in a way, which mainly serves a
powerful class in society. Nevertheless, analysing the culture
around AI technologies with regard to the nature of law with a
philosophical and sociological focus enables us to demonstrate
a utilitarian and authoritarian trend in the adoption of AI
technologies. Mason argues that “virtue ethics is the only
ethics fit for the task of imposing collective human control on
thinking machines” 8 and AI. We will apply virtue ethics to our
discourse around artificial intelligence and ethics.
As expert in AI safety Steve Omonhundro believes that AI is
“likely to behave in antisocial and harmful ways unless they are
very carefully designed.” 9 It is through virtue ethics that this
paper will propose for such a design to be centred around
restorative justice in order to take control over AI and thinking
machines, following Mason’s radical defence of the human and
his critique of current thoughts within trans- and post-
humanism as a submission to machine logic.

The paper will conclude by proposing an alternative
practically unattainable, approach to the current legal system
by looking into restorative justice for AI crimes, 10 and how the
ethics of care could be applied to AI technologies. In conclusion
the paper will discuss affect 11 and humanised artificial
intelligence with regards to the emotion of shame, when
dealing with AI crimes.
In order to discuss AI in relation to personhood this paper
follows the descriptive psychology method 12 of the paradigm
case formulation 13 developed by Peter Ossorio. 14 Similar to how
some animal rights activists call for certain animals to be
recognised as non-human persons, 15 this paper speculates on
the notion of AI as a non-human person being able to reflect on
ethical concerns. 16 Here Wynn Schwartz argues that “it is
reasonable to include non-humans as persons and to have
legitimate grounds for disagreeing where the line is properly
drawn. In good faith, competent judges using this formulation
can clearly point to where and why they agree or disagree on
what is to be included in the category of persons.” 17
In the case of AI technologies we ask whether the current
vision for the adoption of AI technologies, a vision which is
mainly supporting the military-industrial complex through vast
investments in army AI, 18 is a vision that benefits mainly
powerful elites. In order to discuss these questions, one has to
analyse the history of AI technologies leading to the kind of
‘humanised’ AI system this paper posits. The old-fashioned
approach, 19 some may still say contemporary approach, was to
primarily research into ‘mind-only’ 20 AI technologies/systems.
Through high level reasoning, researchers were optimistic that
AI technology would quickly become a reality.
Those early AI technologies were a disembodied approach
using high level logical and abstract symbols. By the end of the
80s researchers found that the disembodied approach was not
even achieving low level tasks humans could easily perform. 21
During that period many researchers stopped working on AI
technologies and systems, and the period is often referred to as
the “AI winter”. 22
Rodney Brooks then came forward with the proposition of
“Nouvelle AI”, 23 arguing that the old-fashioned approach did

not take into consideration motor skills and neural networks.
Only by the end of the 90s did researchers develop statistical
AI systems without the need for any high-level logical
reasoning; 24 instead AI systems were ‘guessing’ through
algorithms and machine learning. This signalled a first step
towards humanistic artificial intelligence, as this resembles
how humans make intuitive decisions; 25 here researchers
suggest that embodiment improves cognition. 26
With embodiment theory Brooks argued that AI systems
would operate best when computing only the data that was
absolutely necessary. 27 Further in Developing Embodied
Multisensory Dialogue Agents Michal Paradowski argues that
without considering embodiment, e.g. the physics of the brain,
it is not possible to create AI technologies/systems capable of
comprehension.
Foucault’s theories are especially helpful in discussing how
the “rule of truth” has disciplined civilisation, allowing for an
adoption of AI technologies which seem to benefit mainly the
upper-class. But then should we think of a notion of ‘deep-truth’
as the unwieldy product of deep learning AI algorithms?
Discussions around truth, Foucault states, form legislation into
something that “decides, transmits and itself extends upon the
effects of power” 28 . Foucault’s theories help to explain how
legislation, as an institution, is rolled out throughout society
with very little resistance, or “proletarian counter-justice” 29 .

Foucault explains that this has made the justice system and
legislation a for-profit system. With this understanding of
legislation, and social justice, one does need to reflect further
on Foucault’s notion of how disciplinary power seeks to express
its distributed nature in the modern state. Namely one has to
analyse the distributed nature of those AI technologies,
especially through networks and protocols, so that the link can
now be made to AI technologies becoming ‘legally’ more
profitable, in the hands of the upper-class.
In Protocol, Alexander Galloway describes how these
protocols changed the notion of power and how “control exists
after decentralization” 30 . Galloway argues that protocol has a
close connection to both Deleuze’s concept of control and
Foucault’s concept of biopolitics 31 by claiming that the key to
perceiving protocol as power is to acknowledge that “protocol
is an affective, aesthetic force that has control over life itself.” 32
Galloway suggests that it is important to discuss more than the
technologies, and to look into the structures of control within
technological systems, which also include underlying codes and
protocols, in order to distinguish between methods that can
support collective production, e.g. sharing of AI technologies
within society, and those that put the AI technologies in the
hands of the powerful few. 33 Galloway’s argument in the
chapter Hacking is that the existence of protocols “not only
installs control into a terrain that on its surface appears
actively to resist it” 34 , but goes on to create the highly
controlled network environment. For Galloway hacking is “an
index of protocological transformations taking place in the
broader world of techno-culture.” 35
Having said this, the prospect could be raised that
restorative justice might offer “a solution that could deliver
more meaningful justice” 36 . With respect to AI technologies,
and the potential inherent in them for AI crimes, instead of
following a retributive legislative approach, an ethical

discourse, 37 with a deeper consideration for the sufferers of AI
crimes should be adopted. 38 We ask: could restorative justice
offer an alternative way of dealing with the occurrence of AI
crimes? 39
Dale Millar and Neil Vidmar described two psychological
perceptions of justice. 40 One is behavioural control, following
the legal code as strictly as possible, punishing any
wrongdoer, 41 and second the restorative justice system, which
focuses on restoration where harm was done. Thus an
alternative approach for the ethical implementation of AI
technologies, with respect to legislation, might be to follow
restorative justice principles. Restorative justice would allow
for AI technologies to learn how to care about ethics. 42 Julia
Fionda describes restorative justice as a conciliation between
victim and offender, during which the offence is deliberated
upon. 43 Both parties try to come to an agreement on how to
achieve restoration for the damage done, to the situation
before the crime (here an AI crime) happened. Restorative
justice advocates compassion for the victim and offender, and a
consciousness on the part of the offenders as to the
repercussion of their crimes. The victims of AI crimes would

not only be placed in front of a court, but also be offered
engagement in the process of seeking justice and restoration. 44
Restorative justice might support victims of AI crimes better
than the punitive legal system, as it allows for the sufferers of
AI crimes to be heard in a personalised way, which could be
adopted to the needs of the victims (and offenders). As victims
and offenders represent themselves in restorative conferencing
sessions, these become much more affordable, 45 meaning that
the barrier to seeking justice due to the financial costs would
be partly eliminated, allowing for poor parties to be able to
contribute to the process of justice. This would benefit wider
society and AI technologies would not only be defined by a
powerful elite. Restorative justice could hold the potential not
only to discuss the AI crimes themselves, but also to get to the
root of the problem and discuss the cause of an AI crime. For
John Braithwaite restorative justice makes re-offending
harder. 46
In such a scenario, a future AI system capable of committing
AI crimes would need to have knowledge of ethics around the
particular discourse of restorative justice. The implementation
of AI technologies will lead to a discourse around who is
responsible for actions taken by AI technologies. Even when
considering clearly defined ethical guidelines, these might be
difficult to implement, 47 due to the pressure of competition AI
systems find themselves in. That said, this speculation is
restricted to humanised artificial intelligence systems. The
main hindrance for AI technologies to be part of a restorative
justice system might be that of the very human emotion of
shame. Without a clear understanding of shame it will be
impossible to resolve AI crimes in a restorative manner. 48

Thus one might want to think about a humanised symbiosis
between humans and technology, 49 along the lines of Garry
Kasparov’s advanced chess, 50 as in advanced jurisprudence. 51 A
legal system where human and machine work together on
restoring justice, for social justice. Furthering this perspective,
we suggest that reflections brought by new materialism should
also be taken into account: not only as a critical perspective on
the engendering and anthropomorphic representation of AI, but
also to broaden the spectrum of what we consider to be justice
in relation to all the living world. Without this new perspective
the sort of idealized AI image of a non-living intelligence that
deals with enormous amounts of information risks to serve the
abstraction of anthropocentric views into a computationalist
acceleration, with deafening results. Rather than such an
implosive perspective, the application of law and jurisprudence
may take advantage of AI’s computational and sensorial
enhanced capabilities by including all information gathered
from the environment, also that produced by plants, animals
and soil.


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