Outline of a Novel Approach for Identifying Ethical Issues in Early Stages of AI4EO Research

Abstract

In the EU, expert groups have done a great deal of work in compiling numerous “ethics guidelines ” for AI. However, recent academic research suggests that these guidelines are not practically useful for academic researchers. Making ethically mindful choices at very early stages of research can help reduce delays and expenses. It can also permit more efficient development of beneficial applications to help solve real-world problems or accomplish the United Nations Sustainable Development Goals (UN SDGs). To support early identification of ethical issues in AI4EO research, this article recommends a novel approach to classifying and identifying ethical issues, based on Eastern and Western philosophical thought and existing theories of ethics.

Full text

Outline of a Novel Approach for Identifying Ethical Issues in Early Stages of AI4EO Research
Mrinalini Kochupillai
1
I Introduction
Academic research and literature
discussing ethical issues in Earth Observation (EO)
or Remote Sensing (RS) research are scant. Yet,
ethical concerns, particularly those linked to
privacy [1, 2], explainability and bias [3, 4], are
growing in relevance as AI and Machine Learning
(ML) models are adopted to study and analyze
petabytes of EO/RS data (hereinafter referred to as
“AI4EO research”). AI/ML models have been used
in EO and RS sciences for decades [5]. However,
ethical issues take center stage as the resolution of
EO/RS data increases rapidly, and as newer sources
of data are fused with EO/RS data to achieve better
results at lower costs and greater speeds.
Nevertheless, not all ethical issues can be
identified in the present – partly because of rapid
technological evolution and almost blind focus on
innovation as an end in itself [8], and partly because
of uncertainties inherent in AI4EO research
methods, analysis and results [6]. Real-world
application of research findings also give rise to
uncertainties vis-à-vis ethical impact.
In the EU, expert groups have done a great
deal of work in compiling numerous “ethics
guidelines
2
” for AI. However, recent academic
research [7, 8] and surveys conducted by the author,
suggest that these guidelines are not practically
1
Affiliation: Data Science in Earth Observation, Technical University of Munich, Willy-Messerschmitt-Str. 1, 82024
Taufkirchen/Ottobrunn, Germany.
Acknowledgement: The work is funded by the German Federal Ministry of Education and Research (BMBF) in the framework of the
international future AI lab "AI4EO -- Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond" (Grant
number: 01DD20001). The author wou ld like to thank Ms. Julia Köning er for excellent research ass istance.
2
At the outset, it is necessary to distinguish between general ethics guidelines for researchers (i.e. guidelines on how to conduct research
e.g. taking informed consent from research participants/interviewees etc.) and technology specific ethics guidelines (i.e. guidelines on
broader ethical issues to be avoided or kept in mind when deploying research results and applications from any technology for general or
specific real-world use). In this article, “ethics guidelines” refer to the latter.
useful for academic researchers, particularly when
they are engaged with “fundamental” or
“application agnostic” research.
Yet, making ethically mindful choices at
very early stages of research can help reduce delays
and expenses. It can also permit more efficient
development of beneficial applications to help
solve real-world problems or accomplish the
United Nations Sustainable Development Goals
(UN SDGs). To support early identification of
ethical issues in AI4EO research, this article
recommends a novel approach to classifying and
identifying ethical issues, based on Eastern and
Western philosophical thought and existing
theories of ethics. Based on this approach, a step
wise guide can be created that helps researchers
identify major ethical issues under concrete and
comprehensive “action categories/stages.” These
categories can also be easily modified based on the
specific technology and use-case at hand.
II A novel approach to identify and
classify ethical issues in emerging technologies
Ethical approaches emerging from the
Western world can be classified broadly into two
categories, namely, those based on (i) the
consequences of an action (also known as
consequentialist or teleological approach) and (ii)
the nature or duty of the human actor (also known

as deontological approach). Scholars also
recommend approaches that combine both
categories [13, 14]. Within these approaches,
several theories of ethics have evolved over time,
including the ethical egotism [15], Utilitarianism
[16], theory of rights and justice [17], virtue ethics
[18], feminist ethics [19] etc.
Going beyond duties and consequences,
Eastern philosophy also places considerable
emphasis on the interlinked concepts of Karma
(action, its causes and consequences) and Dharma
(personal duty, social responsibility and religious
dictates). Considerable emphasis is also placed on
“our three powers” (Shaktis): Power of (i)
intention/desire/will (“Icchha Shakti”), (ii) action
(“Kriya Shakti”), and (iii) knowledge/human values
(“Gyan Shakti”). In Indian mythology, symbols
associated with Lord Muruga vividly describe the
link between these three powers: Knowledge and
human values are meaningless if they are
unaccompanied by appropriate will, i.e. desire to
act, and constructive action. Similarly, will and
action must be guided by knowledge or human
values for long term benefits for oneself and society
[17], [18].
In AI for EO, the presence of a plethora of
uncertainties mandates the need to look beyond
consequences, at duties (social responsibilities),
intentions, knowledge/ human values, as well as
concrete, present action: indeed, in several (if not
most) instances, the (long term) consequences of
conducting research, and of translating research
results to concrete products or services, will remain
unknown until a much later date. A focus on
consequentialist theories may not provide
meaningful guidance at early stages of research.
Being aware of one’s broad duties, while
very significant, may also not be adequate. Indeed,
guidelines for research ethics, which provide, inter
alia, methodological and procedural guidelines on
how or how not to conduct research (e.g. obtaining
informed consent from research participants,
avoiding plagiarism), comprehensively enumerate
the duties of researchers. Fundamental principles of
ethics, e.g. honesty, integrity and fairness, are also
basic duties or characteristics of any ethical
researcher. However, these duties are abstract and
inadequate to guide researchers working with
emerging technologies like AI4EO.
In this situation, five practical steps can
provide concrete guidance to identify, flag and
avoid ethical issues in early stages of research.
These five steps are not linear, but rather iterative
and circular, spanning the entire research duration:
First, scrutinizing and becoming aware of
the concrete (long and short term)
will/intention/desire driving the research. Second,
determining whether these intentions/desires are
aligned with one’s own conscience, with human
rights and contribute constructively to the UN
SDGs or to overcoming concrete societal problems.
Third, identifying and categorizing the specific
actions taken as part of the research (“action
categories/stages”). Fourth, ensure that every
action (e.g. steps of research) is aligned with and
aimed at accomplishing the expressed desire/will.
Finally, checking to ensure that each action(s) is in
harmony with human conscience and a universally
acceptable set of human values.
In present times, unlike the Universal
Declaration of Human Rights (UDHR), Universal
human values have not been consolidated into a
single, comprehensive international document.

While a few scattered efforts in this direction are
notable [19-21], in the absence of a globally
accepted document, practical ethical issues
identified in existing literature, the UDHR, as well
as the UN SDGs can contribute significantly in
steering research endeavors towards ethically
aware goals, objectives (intention/will) and actions.
While broad principles of ethics are, arguably,
already well-known to researchers, what is missing
is a method to practically use or apply these
principles in various stages of research, especially
when dealing with large quantities of data from
various sources. The next section provides a step by
step guide for researchers.
III Identifying Ethical Issues in
AI4EO research: Step wise guidance
In the broader contexts of AI/ML (and, to
some extent, EO/RS) research, the most significant
ethical issues identified in published literature
include privacy, bias, uncertainty and error, stigma,
(national) security, data veracity, accountability,
integrity, honesty and fairness [2, 4]; [8, 23-25].
In order to make these abstract ethical
issues/concerns more practically useful for
researchers at early stages of research, the first step,
as discussed above, is for the researcher to identify
and list (e.g. in the form of a mind map) the
intention(s)/will/desire(s) driving her research.
These can be classified as long and short term goals
and objectives of the research itself (e.g. I want to
see the rate of rural-urban migration to support
policy making that prevents or reverts the trends, I
want to create a map of major farming systems of
Europe to support the expansion of
sustainable/organic agriculture), and as personal
goals of the researcher (e.g. I want to complete my
Ph.D. and get a doctor title, I want to get promoted,
I want to help the poor). A detailed, honest and
comprehensive list will already help the researcher
get an understanding of her underlying motivations
and whether they stand the test of her personal
conscience and objective standards found in
published literature, the UDHR and the UN SDGs.
Thereafter, the researcher needs to identify
broad “action categories/stages”. These categories
can be at a general or macro level, or at a specific
or micro level. For example, preliminary
discussions with academic researchers revealed
that research in this field can be broadly categorized
under the following research lifecycle stages
(“AI4EO research lifecycle stages”) (List 1) (i)
Fundamental Research (application agnostic) (ii)
Engineering Research (application agnostic, but
possible focus on specific targets) (iii) Applied
Research (application oriented) and (iv)
Application Specific Research (Industrial research
and innovation for marketable applications)
Second, in AI4EO research, large amounts of
data are typically necessary in any and all of the
above research lifecycle stages. Accordingly, the
most important research steps can be further
categorized under detailed “AI4EO data lifecycle”
stages (List 2), such as (i) Selection of data source
and research dataset (iii) Selection of data analysis
method/approach; (iv) Data labelling; (v) Data
analysis; (vi) Data storage; and (vii) Data
publication or dissemination.
It is necessary to note that Lists 1 and 2 are
merely illustrations of possible “action
categories/stages” in any AI4EO (or other)
research. Academic researcher can and should
modify the above two lists, or create other lists
based on their specific field of research. What is

important is that each “action” step is documented
and aligned with the identified desires/intentions of
the research. Finally, it is recommended that
AI4EO researchers (notwithstanding the research
lifecycle stage at which their work is situated),
work closely with ethics researchers to identify the
broad categories of ethical issues that are most like
to arise, need to be flagged or, where possible,
addressed by technological solutions at the earliest
possible, and at all relevant “action stages”.
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