La Trobe University, BUS2SBY: Sustainability Strategy Analysis

This essay provides a comprehensive analysis of the role of systems thinking in addressing sustainability challenges, specifically focusing on the intersection of gender bias and artificial intelligence. The student explores the concept of systems thinking as a holistic approach to problem-solving, contrasting it with reductionist thinking. The paper examines how AI can inadvertently reinforce gender inequality, using the Iceberg Model to analyze the issue at event, pattern, structure, and mental model levels. The essay argues that the discriminatory practices embedded in AI systems stem from the biases of their human developers. The analysis integrates various sources to support the arguments, concluding that addressing gender bias requires a revamping of AI technology and a critical examination of the human perspectives behind its development.

Running head: SUSTAINABILITY STRATEGY ANALYSIS
Sustainability Strategy Analysis
Topic: Systems Thinking is critical in developing solutions to Sustainability Challenges
Name of the Student
Name of the University
Author Note

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Table of Contents
Introduction................................................................................................................................2
Background of the topic.........................................................................................................2
Integration of the sources into the discussion........................................................................4
Critical analysis presenting the two sides of an argument.....................................................5
To what extent do you agree with the statement?..................................................................7
Conclusion..................................................................................................................................7
References..................................................................................................................................9

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Introduction
The concept of systems thinking is an idea that is build over the approach to integrate
the beliefs that is otherwise thought to be acting differently as separated systems. Simply
putting, it can be stated that the idea behind the development of a whole system that is
integrated with the help of several parts. System thinking has the ability of thinking about the
working of these integral parts working behind the system and how feasibly can it continue to
work with the help of the ideas of the separated systems when they would work as single
modules and not be integrated within the system. This idea of systems thinking is a reserved
developed idea that is contrasting to the reductions and positivist thinking (Stanila 2018). The
formation of a system thinking is what is though about as a holistic approach to the ideation
of a whole system. It concerns the interactions of the different systems and the thinking that
makes it easier to realise how the systems would be acting as isolated operations in thinking
about the sustainability of the entire system. The general thought behind this is to identify the
scope behind the entire systems thinking procedure and how will it add to the unfolding of
the sustainability of the whole system. This is why this understanding needs to be set in with
this research on how the wicked problem in finding a solution to the sustainability challenges
can be met with the impending systems behind the chosen problem about reinforcing gender
equality problems with the Artificial Intelligence technology infiltration in the society. The
entire analysis process would also be tried to be discussed with the help of the Iceberg Model
theory that is based upon the concept of Systems Thinking.
Background of the topic
It has been found since a longer period of time on how the society has underpinned
the women in general. It has been found that all around the world and the enlisting of the
business organizations and their factual data, even today, the women have been handling only

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9 per cent of the directorships in the board of members (Baeza-Yates 2018). The bias about
gender and the societal thinking about the leadership qualities in women have been found to
be even lesser than the factual data that has been found and this is the evidence that the
gender discrimination might be not drastic than before, but even after the influence of general
technological advancements and the claims to be reaching the better situations than before,
gender discrimination is still very much present even at boardrooms in highly acclaimed
organizations (Thompson, Dunn and Calkin 2017). However, there has been better situations
about the educational qualifications of women and it has been found that the women are now
much more qualified than before, might even be more qualified than the men in general who
claim to be extremely in leading an organization than women. The primary reason that this
occurs is the general biasness about the genders.
The general idea behind this is the way by which the entire world thinks that with
better education and with better advanced scenarios in the technological and business field
will reduce the overall problem. However, it was still found that the implementation of highly
advanced technology could not help the total idea of gender discrimination and has been
worsening it further. This would be the systems that has the ability of scanning the biometrics
of the human body and easily discriminate the gender of any person seeking the use of
Artificial Intelligence. For example, this can be a situation where a credit card company can
have an assessment for the clients who would like to apply for a loan. For this, the company
may claim that there would be a proper clarification of the situations that the client is in right
now to find out how the entire system would be clarifying who would be eligible for the loan
application and who would not be eligible. This system can not only identify the analysis for
the situation of the applicants and also it can be possible that there might be situations where
there are chances that the applicant can ha a failure in payment (Addae and Ling 2018). In
this case, there are chances that can immediately lead the situation to further gender bias

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situation. This is because, the company can even set a rule where the single women can be
disallowed their ability of applying for the loan. This will be classified as a gender biased rule
that does not clarify if the woman is capable enough to pay her debts with the company, if the
woman has enough monetary adjustment or if the woman had a clean record with the
company so far. Only on account of the gender and marital status, the woman can be denied
of their loan applications. This is also enhanced by the Artificial Intelligence technologies
where the systems can be identifying the gender of the applicant, which would result in the
denying of services to the particular person, based on their gender.
Integration of the sources into the discussion
There have been several utilizations of different resources that has formed the basis of
the discussion about artificial intelligence and its enhancement of the gender discrimination
factors. These have been supported by the examples and the ideas about the different
situations regarding the AI implementation and the ways by which the technology is
enhancing the gender bias situations in the society (Danks and London 2017). This would be
supported with the integration of different situations that would be working as the
contributory factors behind the establishment of the whole problem. Therefore, analysing the
different situation in this context is also supported by the general context of the systems
thinking. Identifying a possible solution to the problem would prove if the systems thinking
procedure helps in finding out probable solution to any problem feasibly, that is, if there are
possibilities that the systems are sustainable enough.
In the first case, the ability of the Artificial Intelligence systems to assess and identify
the gender of a person and follow the filtration procedures to be screened before the
discriminatory regulations can follow (Hamidi, Scheuerman and Branham 2018). This is
why, the idea behind the AI systems and their ability to discriminate between a man and a
woman is extremely opposed in some cases. Even there are occurrences where the infiltration

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of the artificial intelligence systems can imbibe the career roles of different individuals when
they go on any social platforms for searching jobs. This is why, there has been several
implications about the utility of the job seeking websites that often can make the people
discriminate between the displayed gender of any applicant. It has also been recognized that
the female job applicants often are not displayed the high paying jobs and the men are offered
better paid jobs with higher posts in even highly acclaimed organizations as the algorithms
are developed. Hugely acclaimed sources like LinkedIn and Google have been claimed to
have these discrepancies where the identification of the gender of a person results to the
above-mentioned issues.
There have been other issues noticed about the images of the people used for training
purposes and how these are infiltrating the gender biased issues. This is because, mostly
people develop the image recognition systems in such a way that the identification of the
genders become susceptible to the biased behaviours. Like for the incidences about Microsoft
and Facebook, it has been found that the big data infiltration has proved to be displaying the
females with the gender biased every day scenes like the cooking related posts, where on the
other hand, the men are mostly shown the sports-related posts (Wang and Degol 2017).
Women and men are discriminated in this way according to their genders as they also are
developing their algorithms based on the stereotypical idea where the women are associated
with the works related to the households and the men stronger gender associations are still
related to the men.
Therefore, the ideas that were put forward with the help of the above ideas clearly
state how even after the input of artificial intelligence and more advanced technologies, there
still lies different ways of gender discriminations.

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Critical analysis presenting the two sides of an argument
For the analysis of this situation, it can be said that the utilization of the Iceberg
Model for the identification of the solution with the help of Systems Thinking should be
appropriate (Gürdür and Törngren 2018). Following would be the ideas based on the model
mentioned above for the analysis of the situation to find out if there can be two sides of the
topic of argument:
 Event Level: The artificial intelligence use and the ways by which they also
add up to the discriminations of the gender biasedness even in today’s time
forms the issue and the event for discussion over this argument (Allen and
Kilvington 2018). The solution to the problem is what is being aimed at right
now with this discussion.
 Pattern Level: There have been various incidences that have infiltrated into
adding up to the discriminatory factors of the entire situation. Starting from
stalwarts like Google, LinkedIn, Facebook and other websites, the algorithms
of each of these websites have been found to be extremely discriminatory
towards the genders and they follow the patterns where women and men are
discriminated in this way according to their genders as they also are
developing their algorithms based on the stereotypical idea where the women
are associated with the works related to the households and the men stronger
gender associations are still related to the men.
 Structure Level: The primary cause of the pattern identified is thought to be
not about the machines that are working this way but the minds of the people
that are developing the algorithms. This is not just the problems of the
machines but it is a deeper idea about how discriminatory factors are

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extremely unnoticed within the minds of the people who are developing these
technologies.
 Mental Model Level: Therefore, from the generated patterns and structures, it
can clearly be stated that the machine learning developed form the human
minds are what forming the discriminatory factors in this issue.
Therefore, from the critical analysis of the above discussion, it can be said that the
machines have been technologically advanced yet discriminatory enough, but is never
realised that the people behind the development of the technology is the main responsible
people behind this.
To what extent do you agree with the statement?
This is why, from the above statement that has been generated, it is an agreeable
statement that the people developing the artificial intelligence system are at fault as their
discriminatory minds are being reflected in the technology (Camelia, Ferris and Cropley
2015). The solution can only be attained with the revamping of these technology with further
machine learnings.
Conclusion
Therefore, in conclusion, it can be said that the basis of the gender discrimination
factors and the enhancement of it with the infiltration of Artificial Intelligence systems and
technology much more than getting it reduced can also be solved with the proper
development of a systems thinking module. The basic idea of systems thinking is to think of
the integrated parts of a whole system and make an idea about how the separate systems
would work if these parts had to work out of the system as an isolated one. The basis of the
entire discussion and critical thinking is based out of these ideas only. The basic background
of the topic has been represented after the discussion has been introduced. Then, the basis of

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the research is supported by the integrated factors working behind the entire solution of the
problem so that the systems thinking can be applied as the main framework behind the
description and its sustainability can be described. The patterns of the integrated problems
have been identified so that the solution can be gathered. The Iceberg model for systems
thinking has been presented in this context. This is why, the reasons and the discussion has
been critically assessed to find out the two sides of the argument and also how the discussion
and the result of the discussion agrees or disagrees to the situation.

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References
Addae, B.A. and Ling, Z., 2018. Acculturation of systems thinking for requirements need
analysis for smart energy city development: A case study in Accra. International Journal of
Management, Information Technology and Engineering, pp.9-20.
Allen, W. and Kilvington, M., 2018. An introduction to systems thinking and tools for
systems thinking.
Baeza-Yates, R., 2018. Bias on the Web. Communications of the ACM, 61(6), pp.54-61.
Camelia, F., Ferris, T.L. and Cropley, D.H., 2015. Development and initial validation of an
instrument to measure students' learning about systems thinking: The affective domain. IEEE
Systems Journal, 12(1), pp.115-124.
Danks, D. and London, A.J., 2017, August. Algorithmic Bias in Autonomous Systems.
In IJCAI (pp. 4691-4697).
Gürdür, D. and Törngren, M., 2018. Visual Analytics for Cyber-physical Systems
Development: Blending Design Thinking and Systems Thinking. NordDesign 2018.
Hamidi, F., Scheuerman, M.K. and Branham, S.M., 2018, April. Gender recognition or
gender reductionism?: The social implications of embedded gender recognition systems.
In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 8).
ACM.
Stanila, L., 2018. Artificial Intelligence and Human Rights: A Challenging Approach on the
Issue of Equality. JE-Eur. Crim. L., p.19.

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Thompson, M.P., Dunn, C.J. and Calkin, D.E., 2017, August. Systems thinking and wildland
fire management. In Proceedings of the 60th Annual Meeting of the ISSS-2016 Boulder, CO,
USA (Vol. 1, No. 1).
Wang, M.T. and Degol, J.L., 2017. Gender gap in science, technology, engineering, and
mathematics (STEM): Current knowledge, implications for practice, policy, and future
directions. Educational psychology review, 29(1), pp.119-140.