Systems Thinking's Role in Sustainability Challenges: Gender Bias & AI

This essay explores the critical role of systems thinking in addressing sustainability challenges, with a specific focus on gender bias exacerbated by artificial intelligence (AI). The introduction defines systems thinking as a holistic approach that considers the interconnectedness of system components. The discussion section delves into how systems thinking can be applied to both local and global sustainability issues, emphasizing its utility in tackling complex 'wicked problems' that cannot be solved through isolated interventions. The essay then examines the applications of systems thinking in the context of gender bias and AI, analyzing how AI can perpetuate and amplify existing biases due to biased data. The report highlights the importance of systems thinking in identifying and addressing these biases, proposing solutions that leverage AI to promote gender equality in various sectors, including board directorships and recruitment processes. The conclusion reiterates the value of systems thinking in analyzing complex systems and developing effective solutions to sustainability challenges, particularly those related to the ethical implications of AI.

Running head: SYSTEMS THINKING
Systems Thinking is Critical in Developing Solutions to Sustainability Challenge
Name of the Student
Name of the University
Author’s Note:

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Table of Contents
1. Introduction............................................................................................................................2
2. Discussion..............................................................................................................................2
2.1 Description of Role of Systems Thinking in Local and Global Sustainability Challenge
................................................................................................................................................2
2.2 Explaining about Applications of Systems Thinking on Gender Bias and AI.................4
3. Conclusion..............................................................................................................................7
References..................................................................................................................................9

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1. Introduction
A significant approach that is quite effective for integration and is based on the belief
that various parts of the system might act differently as soon as the system is being isolated
from respective system environment and others parts of the system is termed as systems
thinking (Arnold and Wade, 2015). This specific approach eventually sets out effective
viewing of several systems in a holistic method. Systems thinking approach is solely
responsible to concern regarding major understanding of several systems only after
successfully examining interactions and linkages in elements that are needed to form the
complete system. As soon as this approach is used in practice, the approach is helpful for
encouraging to subsequently explore the inter relationship, boundaries and perspectives
(Mingers, 2014).
It is quite important to successfully develop several solutions to each and every kind
of sustainability challenge. One of these important and significant issues that leads to
sustainability challenges is gender bias (Riley et al., 2017). This could be increased with
implementation of artificial intelligence. Women are eventually under represented in various
economic life spheres, however involvement of technology has made it worse as compared to
any other situation. This report would be providing proper analyses over the approach of
systems thinking to develop subsequent solutions towards sustainability challenge regarding
gender bias as well as artificial intelligence.
2. Discussion
2.1 Description of Role of Systems Thinking in Local and Global Sustainability
Challenge
The approach of systems thinking allows a major process of balancing that has the
core tendency of successfully maintaining equilibrium in any particular system (Domegan et

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al., 2016). Subsequent attention is being provided to this feedback and it is termed as the
major component within the approach. It also enables proper organizational management for
looking at another solutions and wasting resources over the approach that are depicted to be
counterproductive. Systems thinking approach substantially uses computer based simulation
and a collection of graphs and diagrams to model, illustrate as well as predict behaviour of
the systems. Behaviour over graphs or BOT graph is present within the tools, which could be
referred to as several kinds of actions for long period of time (Dunnion and O’Donovan,
2014). A simulation model could be added within the sector, responsible to simulate overall
interactions of the system elements.
This approach of systems thinking has an important role to resolve each and every
global as well as local sustainability challenge. Systems thinking is majorly helpful in
addressing all types of complex or wicked problem situations (AI for Gender Equality
Problems. 2018). These types of local and global problems cannot be resolved by one feature
and this complex system cannot be entirely understood from distinctive perspectives.
Moreover, as the complex system is constantly evolving, the approach of systems thinking
could be oriented towards organizational learning, social learning as well as adaptive
management. In most of the difficult situations, the approach is quite helpful for making the
entire situation effective in a systematic manner and even identifying numerous leverage
points that can be eventually addressed for supporting the changes (Goode et al., 2014). The
approach is also helpful to check the respective connectivity and link within various
elements, involved in the situation to make it quite easy for supporting joined up activities.
The approach of systems thinking provides a framework to understand a complete
decision making cycle (Davis, Dent and Wharff, 2015). The links could introduce the
approach regarding efficient facilitation as well as management to subsequently support the
entire set of understanding regarding the problematic situations, thus bringing out some of the

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major social changes. Sustainability can be referred to the core capability for remaining
sustained, supported, upheld and confirmed. Some of the most significant challenges of
sustainability in the globalized and local sectors and such challenges are needed to be
decreased, with the requirement that high efficiency is eventually gained without any type of
complexity or issue (Waddock, 2014). Scarcities of resources, high population, degradation
in the eco system, losses in bio diversification are referred to as the major sustainability
challenges. Various kinds of issues, which are subsequently bringing out the most distinctive
complexities within modern world majorly involve gender inequality and biasness. Such
issues related to gender biasness must be removed over time, with the expectation that these
gaps in gender equality gets decreased and higher efficiency as well as effectiveness is being
gained (Shaked and Schechter, 2017).
The broad adoption of this particular approach of systems thinking majorly represents
the most effective solution to society for making original progress for transition daunting.
Such systems can easily range to higher complications, hence it becomes extremely easier in
diagnosing and understanding difficult situations (Tejeda and Ferreira, 2014). Systems
thinking approach even allows these users to analyse and discuss these systems to resolve real
world issues or phenomenon. The approach is subsequently concerned regarding awareness
expansion to check all types of relations within portions and not looking on discrete parts.
Evolving of paradigms is one of the most significant problem, which is required to be
efficiently analysed so that high improvements are being brought within the respective
organization (Caliskan, Bryson and Narayanan, 2017).
2.2 Explaining about Applications of Systems Thinking on Gender Bias and AI
AI can be effectively categorized as weak or strong and the respective technology is a
kind of system that could be designed and trained for the task. The core ability of human
cognitive is present for the technology (Jolly and Can, 2015). The strong system of AI has the

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major capability to search for any solution without involving any kind of human interference.
As the expenses of hardware, software and staffing within artificial intelligence is quite high,
there are numerous vendors, who are involving components of AI in obtaining the most
standardized offerings. Moreover, AI is also helpful in providing subsequent access to few
distinctive platforms of AI (Women in Artificial Intelligence. 2019).
The services of this particular technology allow organizations for experimenting with
a technology to fulfil every business purpose. As a result, multiple platforms enable the
clients in making commitments for effective outcomes in the business (Buolamwini and
Gebru, 2018). As there are various tools of artificial intelligence, which are responsible to
present the total range of every new functionality for business; the most significant usability
of artificial intelligence can enhance ethical or moral questions. The main reason behind it is
that deep learning algorithms are eventually underpinning each and every advanced tool of
AI. The high potential towards human biasness is thus inherent and is required to be closely
monitored.
As, according to a survey, observation is being made that only 19 percent of total
board directorship within Europe and United States is eventually hold by girls and women, it
has become one of the most significant sustainability challenges for society (Women in
Artificial Intelligence. 2019). Such kind of gender gaps or discrepancies in the board
management subsequent persists as well as demonstrates regarding the factor that in spite of
women having higher educational knowledge and qualification in respective fields, there are
still existing problems associated to the methodologies and sustainability for dealing with
these problems. The most significant reason to enhance such gender inequality would be that
social bias is present in society (AI for Gender Equality Problems. 2018). Gender bias could
be reinforced by incorporation of AI technology as current and updated data are used to train
the machine. These data could be manipulated and hence is absolutely biased in nature.

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An advanced deployment of artificial intelligence refers to biased data and hence
could easily influence each and every kind of prediction, made by machine (Mingers, 2014).
When the data set for human decision is made, it eventually involves biasness. It could also
include major decision hiring, loan approval and many more. This particular methodology,
through which learning machines can eventually include data sets in the form of texts, images
and voice, is required to ensure that classifier is added to data and the computerized system is
having opportunity to recognize various images as well as capability to relate these images
accordingly (Dunnion and O’Donovan, 2014).
AI is growing in an advanced manner and is even developing various automated
machines and algorithms that ensure about working place extremely effective and efficient as
well as lesser biased (Davis, Dent and Wharff, 2015). Numerous organizations have
eventually implemented the technology of AI into various wok processes, mainly for
recruitment functions and talent management. In few distinctive scenarios, the algorithms
have been sorted with the help of numerous factors related to profile people. Predictions are
required to be made for such algorithms. Talent management as well as hiring systems
comprise of the most significant potential to move the point over gender equality within work
places only after successful utilization of high objective criterion regarding promotion and
recruiting the talents (Shaked and Schechter, 2017). AI experts even consider the respective
technology as a computer system that could easily and promptly perform, learn and even
understand actions. These actions are being seen as the most important factors of intelligence
and incorporating a designed system for analysing sensitive data and also providing
subsequent solutions to the most complex situations.
Three significant methodologies are present, through which AI could easily work that
are autonomous intelligence, augmented intelligence and assisted intelligence (Tejeda and
Ferreira, 2014). These are required for improving the overall automation of routine tasks,

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based on all the clearly defined input. It is also observed that artificial intelligence can serve
as the equalizer to solve issues related to gender inequality. One of the most significant
reasons to this type of effectiveness would be that it can become the respective equalizer for
successfully reducing negative impacts regarding unconscious bias to make data predictions
based on algorithms. Few important and significant examples of AI are present, which can
easily decrease impact of gender bias before improvement of human process (Women in
Artificial Intelligence. 2019). These distinctive algorithms that are solely responsible to
identify board directorship candidates in an extremely accurate manner, eventually enable
male staff after evaluating features, which could be highly overvalued during nomination.
Hiring tools and technologies that are associated to artificial intelligence could easily
match various skills for job descriptions to avoid biasness and also to build up a diversified
state (Caliskan, Bryson and Narayanan, 2017). Such tools are also helpful for searching the
candidates, who were eventually ignored within the process of traditional recruitment only
after searching through several career websites as well as system for applicant tracking. Some
of the most significant companies have been building AI tools that can restrict bias after
assessing applicants based on specific skills and abilities of candidates. AI tool can easily
scan the systems to track applicants so that it becomes much easier to find out the most
appropriate candidates and even remove names from the complete process regarding
avoidance of biasness (Arnold and Wade, 2015). Hence, the system becomes free from bias
and success is highly ensured.
3. Conclusion
Hence, conclusion can be drawn that the holistic approach of systems thinking is
required for effective analyses that emphasizes on a methodology, through which every
constituent part of the system, interlink as well as process the systems for working in time
and also in larger system context. The respective systems thinking approach eventually

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contrasts with traditional approach can allow even breaking of system parts into several
components. It can be used in various research areas as well as is quite popular for any
company. The respective results of system behaviour are required from major effects
reinforcing and balancing the processes. The entire process of reinforcing could eventually
lead in collapsing.
Being biased on gender is referred to as the major problems, which are being
confronted in present world. AI or artificial intelligence is termed as the most significant
solutions to such problems and also for decreasing the sustainability challenge. This
particular technology is a proper simulation of the processes of human intelligence after
incorporation of machines, mainly for computerized systems. These important processes
include self-destruction, reasoning and learning. The most important applications of this AI
mainly include expert system, speech recognition and machine vision. This above report has
provided a brief analysis on the approach of systems thinking to resolve complexities related
to sustainability.

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