Individual Research Report on Sustainability Challenges for BUS2SBY

This report delves into the critical role of systems thinking in addressing global and local sustainability challenges, with a particular focus on gender bias and artificial intelligence. The introduction establishes systems thinking as a holistic approach to understanding interconnected elements within a system, emphasizing its importance in developing solutions to complex issues. The discussion section explores the application of systems thinking to sustainability challenges, specifically in the context of gender bias and artificial intelligence. It highlights the underrepresentation of women in various economic spheres, worsened by technology, and examines how artificial intelligence can exacerbate existing biases due to biased datasets. The report provides detailed analysis on how systems thinking can be used to mitigate these challenges. The conclusion reinforces the value of systems thinking as a comprehensive approach to analyzing and solving complex problems, particularly in the context of sustainability, and highlights the need for continuous adaptation and learning to address these evolving challenges. The report draws upon various sources to support its arguments and provide a well-rounded understanding of the topic.

Running head: SUSTAINABILITY CHALLENGES
Systems Thinking is Critical in Developing Solutions to Sustainability Challenges
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 Role of Systems Thinking in Global and Local Sustainability Challenges.....................2
2.2 Explanation of Application of Systems Thinking on Gender Bias and Artificial
Intelligence.............................................................................................................................4
3. Conclusion..............................................................................................................................7
References..................................................................................................................................9

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1. Introduction
Systems thinking can be referred to as a major approach for integration, which is
completely on the basis of the belief that different parts of a system would be acting
differently whenever it is isolated from the environment of the system or any other part of the
system. This particular approach sets out proper viewing of systems within the most holistic
manner (Arnold and Wade 2015). Systems thinking is responsible for concerning about
subsequent understanding of system after successful examination of linkage and interaction
within elements, which are consisting the entire system. When this approach is in practice, it
helps in encouraging to explore the perspectives, boundaries and inter relationships.
Systems thinking is extremely vital for successful development of numerous solutions
to all types of sustainability challenges. One of such distinctive and significant problem is
gender bias and artificial intelligence (Peters 2014). It is being observed that women are often
underrepresented in several spheres of economic life, however technology has made it much
worse in comparison to previous situation. The following report will be outlining detailed
analysis on systems thinking approach for development of solutions to sustainability
challenges for gender bias and artificial intelligence with relevant details. Details regarding
this particular factor with the help of artificial intelligence will also be highlighted in the
report.
2. Discussion
2.1 Role of Systems Thinking in Global and Local Sustainability Challenges
Systems thinking approach enables a balancing process, which has the tendency in
successful maintenance of equilibrium within any one specific system. Providing major
attention to the feedback is referred to as one of the most significant components in systems
thinking (Davis et al. 2014). It even allows subsequent organizational management in looking

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for any other solution and not only on wasting the resources on an approach, which are being
demonstrated to be counter productive. This particular approach utilizes computerized
simulation as well as a series of graphs and diagrams for modelling, illustrating or even
predicting the systems’ behaviour. Within the tools of systems thinking, there exists graph of
BOT or behaviour over graph that refers to all types of actions of one and more variables for
a longer time period, management flight simulation that utilizes an interactive program for
simulating the overall effects of all types of management related decisions (Mingers 2014).
The respective simulation model is also being added in this sector that is responsible for
simulating the interaction of system components within time.
Systems thinking approach has always played one of the most significant and
important roles in resolving all types of local and global sustainability challenges
(Underwood and Waterson 2014). This particular approach is mainly useful while addressing
any kind of wicked or complex problem situation. Such global as well as local issues could
not be solved by one actor and the complex system could not be completely understood from
a single perspective. Furthermore, since the complex adaptive systems are continuously
changing, this approach is being oriented towards the adaptive management, social learning
and organizational learning. In some of the most difficult situations, systems thinking helps to
make the situation systematically and identify several distinctive leverage points, which could
be addressed in supporting constructive changes (Riley et al. 2017). It is even helpful in
checking the link or connectivity between different elements within the situation so that it
becomes easier to support the joined up actions.
A framework is being provided by the systems thinking approach for understanding
the entire cycle of decision making. These links introduces systems thinking for better
management and facilitation so that it becomes easy to support a collection of understanding
of any problematic situation, hence bringing social changes (Carey et al. 2015). Sustainability

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is the major ability to be confirmed, upheld, supported as well as sustained. There are several
sustainability challenges both in local and global sectors and these challenges are required to
be reduced, so that better effectiveness is being obtained without much complexity. The
resource scarcities, over population, biodiversity losses, degraded ecosystem and many more
are the most significant challenges of sustainability. Other types of issues that are bringing
major complexities in the modern world include gender bias and few others (Domegan et al.
2016). These issues should be eradicated on time, so that gender gaps are reduced and high
effectiveness and efficiency is obtained.
Widespread adoption of the systems thinking approach represents the best solution of
the society to make real progress in daunting of transition. These systems could range in high
complexity and it becomes quite easy to understand as well as diagnose whenever anything is
going wrong. This systems thinking would allow the users in analysing and discussing the
systems in terms of practical context for solving real world problems and phenomenon
(Caliskan, Bryson and Narayanan 2017). This specific approach of systems thinking is
majorly concerned about expansion of the awareness for checking the relations between parts
and not only looking at the discrete parts. Changing the paradigms is a major issue that is
needed to be analysed so that it becomes quite easy to bring better improvements in the
business.
2.2 Explanation of Application of Systems Thinking on Gender Bias and Artificial
Intelligence
Artificial intelligence could be categorized as either strong or weak and this particular
technology is the type of AI system, which is being designed as well as trained for any
specific task and there exists generalized human cognitive ability (Buolamwini and Gebru
2018). The strong artificial intelligence system has the core ability of finding a proper
solution without any type of human intervention. Since, software, staffing and hardware costs

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regarding artificial intelligence could be extremely expensive, several vendors are involving
the components of this specific technology within their standardized offerings and major
access to the platforms of artificial intelligence as a service. This particular service eventually
enables the companies and individuals in experimenting with the technology for several
business purposes as well as multiple platforms eve before making a commitment (Zhao et al.
2017). Since the tools of AI are presenting a range of new functions for the business, a major
utilization of AI could raise ethical questions. It is because the algorithms of deep learning
are underpinning several advanced AI tools. A high potential for human bias is extremely
inherent and should be monitored closely.
Since, it is being observed that only 19% of the total board directorships in the United
States and Europe are being hold by women, it is has become a sustainability challenge for
the entire society. This type of gender gap within board management persists and
demonstrates about the fact that although, women have obtained high educational
qualifications and knowledge in their field, there still lies issues related to sustainability and
methods to deal with such issues (Savulescu and Maslen 2015). The major reason for this
type of gender inequality is that there exists social bias. It is on the verge of being further
reinforced by the technology of artificial intelligence, since present data is being utilized for
training machine to learn are completely biased.
With the better and advanced implementation of AI, the biased data would eventually
influence all types of predictions, which are made by the machine (Rudinger, May and Van
Durme 2017). As soon as a specific data set of human decisions is being made, it
substantially includes bias. It can also involve hiring of decisions, approvals of loans, medical
diagnosis and even grading student examinations. The method, by which machines are
learning, includes considering the data sets in the computer, within the form of voice, images
and text. It is also required to add a classifier to the data and this computer system would

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have learned for recognizing similar images and even have the core ability of associating the
images with a working woman.
The entire field of artificial intelligence is highly growing at a rapid pace and is
developing numerous algorithms as well as automated machines, which show prose to make
the subsequent working place much more efficient and lesser biased (Leavy 2018). Various
companies have introduced artificial intelligence into several procedures of work, majorly for
talent management and recruitment functionalities. In several cases, these algorithms sorted
through various factors to the profile people and the made predictions regarding them. The
systems of talent management and hiring have the major potential for moving the needle on
gender equality in the work places after utilizing higher objective criteria for recruitment as
well as promotion of talents (AI and Gender Bias. 2019). The experts of artificial intelligence
often consider this technology as a computerized system, which can easily understand, learn
as well as perform actions that are seen as requiring intelligence and even a designed system
to understand or analyse confidential data as well as providing solutions for complex problem
solving.
There exists three distinctive methods, by which artificial intelligence can work,
which are assisted intelligence, augmented intelligence and autonomous intelligence. The
assisted intelligence improvises automation of the routine tasks, on the basis of every clearly
defined human inputs. It even assists in making different tasks much easier, however is
limited in completing the task within strict parameters for making final decisions (AI for
Gender Equality Problems. 2018). It requires people to enhance their efficiency after
involving women strength in the organization. Moreover, the augmented intelligence allows
enhancement of decision making after completely relying on the complex partnership of
human development. The programs become quite better in augmenting problem solving as
well as decision making.

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It is being proved that AI could easily server as an equalizer for solving gender bias
issues. The main reason for such effectiveness is that it could become an equalizer and reduce
decisions for those individuals, who are naturally subject to the unconscious biases, for
making subsequent predictions with the data on the basis of algorithms (Rudinger, May and
Van Durme 2017). There are some of the most effective and important examples of artificial
intelligence that could reduce gender bias as well as improve the human processes. The
algorithms, which are responsible for identifying the candidates of board directors much
more accurately than normal people do, eventually allow the male employees in evaluation of
the characteristics that they might be overvalued while nominating the board directors.
The hiring tools related to AI, which provide good description of jobs, can match the
skills towards job descriptions for the core purpose of avoiding bias and building much more
diversified slate. It is even helpful in finding candidates, who were being ignored in the
traditional recruitment procedure after searching through career web sites and applicant
tracking system. Few popular organizations are eventually building the tools of AI, which
restrict bias after assessment of the applicants on the basis of specified abilities, skills and
data of the employees (Leavy 2018). One of the tool of AI eventually scans these systems for
applicant tracking for finding the most suitable candidates as well as removal of the names
from the entire procedure to avoid bias. The recruitment tasks are being made completely
automatic for successfully reducing bias from the system and ensuring success.
3. Conclusion
Therefore, from the above discussion, it can be concluded that systems thinking is a
holistic approach for better analysis, which eventually focuses on the method, by which the
constituent parts of a system interrelate and process of systems working within time and in
the context of large system. This particular approach of systems thinking contrasts with
traditional analyses that allows breaking down of the systems into various elements. It could

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be utilized in in all research areas and is extremely popular for all organizations. The system
behaviour results from the subsequent effects of balancing as well as reinforcing procedures.
A reinforcing process can lead to collapse.
Gender bias is one of the most significant issues that is being faced in today’s world.
Artificial intelligence or AI can be one of the greatest solutions to these types of issues and
even for reducing such sustainability challenges. AI is the simulation of human intelligence
procedures through machines, majorly for the computer systems. Such distinctive procedures
mainly involve learning, reasoning and even self destruction. The major applications of
artificial intelligence involve machine vision, speech recognition and expert systems. The
above provided report has clearly outlined a detailed analysis on systems thinking approach
for resolving the issues of sustainability with proper details.

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References
AI and Gender Bias. 2019. [online]. Accessed from https://www.catalyst.org/research/trend-
brief-gender-bias-in-ai/ [Accessed on 28 August 2019].
AI for Gender Equality Problems. 2018. [online]. Accessed from
https://theconversation.com/artificial-intelligence-could-reinforce-societys-gender-equality-
problems-92631 [Accessed on 28 August 2019].
Arnold, R.D. and Wade, J.P., 2015. A definition of systems thinking: A systems
approach. Procedia Computer Science, 44, pp.669-678.
Buolamwini, J. and Gebru, T., 2018, January. Gender shades: Intersectional accuracy
disparities in commercial gender classification. In Conference on fairness, accountability and
transparency (pp. 77-91).
Caliskan, A., Bryson, J.J. and Narayanan, A., 2017. Semantics derived automatically from
language corpora contain human-like biases. Science, 356(6334), pp.183-186.
Carey, G., Malbon, E., Carey, N., Joyce, A., Crammond, B. and Carey, A., 2015. Systems
science and systems thinking for public health: a systematic review of the field. BMJ
open, 5(12), p.e009002.
Davis, M.C., Challenger, R., Jayewardene, D.N. and Clegg, C.W., 2014. Advancing socio-
technical systems thinking: A call for bravery. Applied ergonomics, 45(2), pp.171-180.
Domegan, C., McHugh, P., Devaney, M., Duane, S., Hogan, M., Broome, B.J., Layton, R.A.,
Joyce, J., Mazzonetto, M. and Piwowarczyk, J., 2016. Systems-thinking social marketing:
conceptual extensions and empirical investigations. Journal of Marketing
Management, 32(11-12), pp.1123-1144.

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Leavy, S., 2018, May. Gender bias in artificial intelligence: The need for diversity and gender
theory in machine learning. In Proceedings of the 1st International Workshop on Gender
Equality in Software Engineering (pp. 14-16). ACM.
Mingers, J., 2014. Systems thinking, critical realism and philosophy: A confluence of ideas.
Routledge.
Peters, D.H., 2014. The application of systems thinking in health: why use systems
thinking?. Health Research Policy and Systems, 12(1), p.51.
Riley, B., Willis, C., Holmes, B., Finegood, D.I.A.N.E.T., Best, A.L.L.A.N. and McIsaac, J.,
2017. Systems thinking and dissemination and implementation research. Dissemination and
Implementation Research in Health: Translating Science to Practice,.
Rudinger, R., May, C. and Van Durme, B., 2017, April. Social bias in elicited natural
language inferences. In Proceedings of the First ACL Workshop on Ethics in Natural
Language Processing (pp. 74-79).
Savulescu, J. and Maslen, H., 2015. Moral Enhancement and Artificial Intelligence: Moral
AI?. In Beyond Artificial Intelligence (pp. 79-95). Springer, Cham.
Underwood, P. and Waterson, P., 2014. Systems thinking, the Swiss Cheese Model and
accident analysis: a comparative systemic analysis of the Grayrigg train derailment using the
ATSB, AcciMap and STAMP models. Accident Analysis & Prevention, 68, pp.75-94.
Zhao, J., Wang, T., Yatskar, M., Ordonez, V. and Chang, K.W., 2017. Men also like
shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint
arXiv:1707.09457.