=Paper=
{{Paper
|id=Vol-2347/5-Slavkovik
|storemode=property
|title=Autonomous Yet Moral Machines
|pdfUrl=https://ceur-ws.org/Vol-2347/5-Slavkovik.pdf
|volume=Vol-2347
|authors=Marija Slavkovik
|dblpUrl=https://dblp.org/rec/conf/c3gi/Slavkovik18
}}
==Autonomous Yet Moral Machines==
https://ceur-ws.org/Vol-2347/5-Slavkovik.pdf
Autonomous yet moral machines
Marija SLAVKOVIK 1 ,
Abstract. Autonomous machines, both software and embodied artificial intelligent
(AI) agents, will continue to relieve us of the burden that are monotone, repetitive,
or dangerous tasks. The more autonomous machines inhabit our everyday life, the
more we need to concern ourselves with the extent of disruption that this technology
ushers. This is a brief overview of the different fields tackling ethical concerns with
respect to AI research and applications
1. Background
Recent advances in applications of artificial intelligence (AI), specifically, in machine
learning and deep neural networks, can create the illusion that AI is a recent technological
advance. AI has been an established research field since 1956 [22], but ethical concerns
with respect to AI have only gotten a serious scientific attention since the begining of this
century [19,28,4]. What has changed is not the existence, or reality, of AI applications,
but who the users and environment of these AI applications are today [9].
Last century AI applications are either powerful enough to inflict physical harm to
people, or “toys” that have very limited range of abilities, such as the autonomous vac-
uum cleaners [25]. Further more, the first category has been limited for use by specially
trained professionals and restricted in movement to a so called working envelope - con-
strained space that only trained specialists are allowed to enter. Examples of this category
of machines are industrial robots [20], automated subway systems, which have been in
operation for the past forty years2 , complex scheduling systems using constraint satis-
faction programming [24], etc. A less obvious example are autonomous intelligent soft-
ware solutions such as financial management decision aid systems [30]. Such decision
aid systems are purpose built to be used by professionals.
In contrast today, we not only have AI applications that are powerful enough to
inflict pain and not restricted to a controlled environment, but also software and hardware
that is capable of a certain degree of autonomy and is available off-the-shelf to be used
in virtually infinite contexts of operation. Examples include the ubiquitous self-driving
cars [17], but also smart software and devices [18], such as smart speakers [16]. Decision
aid systems have also evolved to include prediction of future behavior based on data that
may not have been specifically collected by domain experts for this purpose [14].
1 Marija Slavkovik, University of Bergen, Norway; E-mail: marija.slavkovik@uib.no
2 http://www.railjournal.com/index.php/metros/uitp-forecasts-2200km-of-automated-metros-by-2025.
html
2. What do we talk about when we talk about AI ethics?
Unlike any other technology, AI raises special societal ethical concerns by virtues of
being capable of, however limited, autonomy and unconstrained interaction with people
and property.
Responsible AI, but also accountable and transparent AI ,[11,10,23,26], concerns
ethical issues of using AI methods or conducting AI research. As any other powerful tool
that extends human abilities, AI can be used to benefit people, but also due to malice or
negligence it can cause harm. Responsible AI is not only concerned with ensuring the
ethics of AI research and applications as a whole. It is also concerned that the right mount
of attention is used to ensure that the AI application does not build upon or propagate
existing unethical practices in our society.
Explainable AI [1,21] is concerned with ensuring that the decisions and actions of
an autonomous systems have explanations comprehensible for the people who use those
decisions, coordinate with those actions, or are affected by them. The explainability of a
system is particularly a concern in the use of those machine learning methods, such as
deep learning, for which there is no built in clear or transparent way to explain why a
particular prediction is made given particular data. To a certain extent, explainable AI is
also about ensuring that a human is always kept “in the loop” as a safe-guard that human
intentions and AI actions are aligned [29].
Machine ethics or artificial morality [19,28,4,9] is explicitly concerned with the
implementation of moral behavior in autonomous systems. In ethically sensitive situa-
tions, where the system makes decisions and actions that require a sensitivity to right
and wrong, we need to make sure that the machine has such sensitivity or is somehow
constrained from choosing an unethical option.
3. What is a moral machine?
Human activities, regardless of how monotonous and repetitive they are, consist not only
of a task, but also of mindfulness who we are as an agent executing that task and how
our actions affect the environment in which they are executed. We do not seize to be a
moral agent regardless of what we are doing. What does it mean for a person to be a
moral agent and her actions to be good or bad, is the domain of moral philosophy [15].
When a machine is programmed to replace a human activity, explicit concerns have
to be taken not to engineer away the ethical impact that activity has on its environment.
In the past, when AI applications have been implemented in controlled operated environ-
ments or designed for trained users, these ethical concerns could be engineered by pre-
dicting and constraining any context in which they might arise. In today’s applications,
this approach is no longer viable.
While the question of can a machine be a moral agent is still open among moral
philosophers [2,13], it is slowly becoming clear that in machines we speak of degrees of
moral sensitivity. Four categories of ethically sensitive agents have been forward by [19]:
ethical impact agents, implicit moral agents, explicit moral agents and full moral agents.
Wallach and Allen [27, Chapter 2] distinguish between operational morality, functional
morality, and full moral agency. The last category in both works refers to human level of
morality, what ever that may mean.
Agents with ethical impact are autonomous systems that do not make ethical
choices. Predicting whether a convict is likely to re-offend if released on parole is not a
moral decision. However, these systems do change the moral fabric of the environment in
which they exist. A system that predicts re-offense, or a system that recommends whether
you should be given a loan, can do wrong by perpetuating a bias against a certain group
of citizens [6].
Implicit moral agents and agents with operational morality are autonomous systems
that do make moral decisions, but they do so by following constraints specified by a
human operator. Even if fully autonomous such systems can only “recognize” and react
to a morally sensitive situation if explicitly programmed to do so [12]. For example,
Apple’s Siri would not give answers to explicit requests for suicide methods, but cannot
handle implicit “calls for help”3 . Explicit and functional moral agents have the capacity
to use their autonomy to recognize and act upon a morally sensitive situation, advancing
beyond their explicit programming. Machine ethics is particularly concerned with the
behavior of implicit and explicit agents.
4. Certifying the behavior of moral agents
How to implement moral artificial agents, namely the implicitly and explicitly moral
agents, remains a challenge for both researchers and engineers. However, implementa-
tion alone is not sufficient. A moral agent, or a machine such an agent controls, needs to
be certified before it is allowed to impact society. Certification informs consumers and
experts of the properties of a product, a system, or a person in a position of responsibil-
ity. Each of the different types of moral agents should be subject to a different process
of certification, just as the process of certifying surgeons is different than the process of
certifying toasters.
In the case of ethical impact agents, it is not the agent itself but how this agent is
used that has a moral impact on society. Explainability, transparency and accountability
of AI particularly applies to this class of moral agents and could be used in the process
of their certification.
It has been argued that formal verification can play an important role in certifying
implicitly ethical agents [8]. Formal verification is the process of using formal methods
of mathematics to prove or refute the correctness of intended algorithms underlying a
system with respect to a certain property. The challenge in verification is to formally
specify the property of ethical behavior.
With explicit ethical agents it is least clear what the process of certification should
entail. If the agent is adaptive, then we need to show that not only does the agent behave
ethically at a given moment, but also that the agent will not adapt towards unethical
behavior. It has been proposed that agents can be tested for ethical behavior by using a
moral Turing test, a version of the Turing test involving asking questions of morality to
the agent and comparing the answers to those obtained from a moral expert [2,3]. It has
also been argued that the moral Turing test is not sufficient [5].
The question of how to make autonomous yet moral machines remains widely open
from philosophical and AI research aspects, however it is also a legal problem [7]. De-
3 https://www.cnbc.com/2018/06/06/siri-alexa-google-assistant-responses-to-suicidal-tendencies.html
ciding who decides which morality should an autonomous system implement and who
should such a system be explainable or accountable to, remains a wider societal issue.
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