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 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 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 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.

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 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 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.

References

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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.

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.