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Analysis and Evaluation of the Chelsea

Part A

What the CTO as well as IT Manager decide will have far-reaching effects on compliance with the ACS Codes of Professional Ethics and Conduct alongside the Australian Privacy Act, which govern the handling of personal information.

By opting for the less secure alternative initially, they have increased the likelihood that confidential data would be accessed by unauthorized people, including employees and external stakeholders.

Data subjects have a right to expect that their personal information would be protected against any use, publication, or access that would be contrary to the Acts (Bowern et al., 2006). As a start, they have increased the possibility that unauthorized parties may get access to critical information by selecting an approach with a reduced security level. Stakeholders may be either inside or external to the organization. This may be in contravention of the Act, which mandates that persons' personal information be secured against unauthorized access, utilization, or disclosure. 

Second, according the ACS's code of ethics, members must put their customers' needs before their own. The method Chelsea has proposed provides the best possible degree of security; hence she meets this criterion. While her plan may be in the finest interest of their client, the CTO and IT Manager have opted not to implement it. Because of this, they risk having a complaint lodged against them. In line with the ACS's Code of Ethics, all of the organization's members are obligated to put their clients' needs first. Chelsea performed so by recommending the safest course of action. The Codes of Ethics as well as Professional Conduct makes mention to the Australian Computing Society (ACS). An ethical code is in place as per the ACS legislation (Swinburne.edu.au, 2022). As an ACS member, it is your responsibility to uphold and improve the professional norms of respectability, esteem, and effectiveness. Other prerequisites include being a law-abiding, upstanding member of society. The public interest, as stated by the code of behavior known as "The primacy for public interests," must always come before the private one. The public interest provision of this law shall govern the resolution of all disputes. Public interest subjects include those related to health, safety, and the public surroundings (Rogerson, Weckert, & Simpson, 2000). She offered a recommendation, but the Chief Technical Officer and IT Manager rejected it, presumably against the interests of their customer. As a result, they're fair game for a complaint.

Thirdly, the decision taken by IT manager and CTO might lead to a drop in consumer trust due to consumers' fears that their private information could fall into the wrong hands. As a consequence, the company risks losing customers and income.

If Chelsea is viewed as having warned against the completed option, she might be held responsible for any security breaches that occur as a consequence of the decision. Although Chelsea could be viewed as missing the capacity to persuade or influence her clients to choose the best decisions for their business, the decision selected by the CTO as well as IT Manager might have an effect on her career. Her professional standing and opportunities may suffer as a result of this.

Following this Code, the common interest takes precedence over private, individuals, and special interests, therefore the public goal ought to be the deciding factor in any disagreement. Protecting the interests of your personal stakeholders is a necessary part of your profession if they do not conflict with your duty and loyalty to the public. The proliferation of information and communication technologies has had profound effects on our culture and way of life. Although the overall impact of ICT has been good, the technology is not without flaws and nor will it be for the foreseeable future. An honest approach to work can help you spot and mitigate such unintended consequences. Neither the general public's nor your stakeholders' trust in your profession should be betrayed.

Professionalism demands absolute integrity and transparency in all decisions and actions. There will undoubtedly be times in your professional life when it will appear beneficial to be dishonest in some capacity. Only accept jobs you know you are qualified to do, and don't be afraid to ask for help from more experienced coworkers when you need it. Never lie about your abilities for the sake of seeming more capable than you really are. This is distinct from accepting a job that will need skills that you don't now possess.

In conclusion, there are a number of ways in which the CTO along with IT Manager's decision has violated the Australian Privacy Act alongside the ACS Codes of Professional Ethics and Conduct with regard to the handling of sensitive information.

Part B

In this scenario, Chelsea should turn down the suggested fix. This recommendation is supported by several arguments. To begin, the client's less secure option leaves their team and additional stakeholders vulnerable to gaining unauthorized access to sensitive information.

Secondly, the technique makes the network more vulnerable to assault from the outside by hackers. Chelsea has been an outspoken supporter of upgrading the system's security to an optimal level (Martin & Rice, 2011). The proposed solution is not in the customer's or the business's best interest. In view of these conditions, it is highly suggested that Chelsea reject down the offered remedy. There are a number of reasons for making this suggestion. In the beginning, considering the customer selected the less secure option, employees and other interested parties may easily circumvent security to access sensitive data. The selected method makes the system more vulnerable to assaults from outside by hackers. Chelsea has also advocated strongly for the system to be equipped with maximum security features. Implementing the suggested fix is not in the greatest interests of the business or the client.

It is strongly suggested that Chelsea does not use the proposed remedy at this time. There are a lot of good reasons to take this recommendation to heart. The customer opted for a less secure solution to begin with. This indicates that crucial information may be accessed by unauthorized people, including personnel, both within and outside the organization. Thirdly, the technique raises vulnerability to attacks from outside parties, particularly cybercriminals. And lastly, Chelsea firmly feels that the system ought to provide the maximum degree of security achievable, contrary to what her statement suggests (Zimmer, 2020). Therefore, it is not within the client's or the company's best interest to execute the offered solution at the present moment.

Part C

K- Anonymity confidentiality is used as a measure of security. Businesses and other institutions now collect and store more personal information than ever before. Information like this helps companies run smoother and better serve their customers. Nevertheless, security professionals and regulatory organizations throughout the globe have made it a major goal to discover a solution to protect the utility of data while adequately decreasing the chance of confidential information being disclosed. It happens because many bad actors want to get their hands on private data and then utilize it to track down its origin (S, S, and P, 2015).

k- Anonymity prevents hackers and other malicious parties from identifying their targets via a process called "re-identification," or tracking data until it reaches the individual who it is linked with in the real world. Sensitive information (medical documents, prescriptions, bank account numbers, passwords, etc.) may coexist alongside personally identifiable information (name, postal code, gender, etc.). The privacy of a person may be compromised if identifying data is paired with sensitive data and then misused (Niu et al., 2014). K-anonymity works to ensure that the two sets of information cannot be linked in any way.

When K equals 5, there are five rows in the mentioned table, one for each conceivable combination. Several quasi-identifier qualities are used in a generic sense to achieve this.

Information which cannot be divulged appears within the sensitive column. Which table has the shortest necessary key (k) for data anonymization?

Identity

age postcode

Gender

Bracket of income

1

20

25

M *

2

20

25

M *

5

20

25

M *

10

20

25

M *

13

20

25

M *

4

30

35

F *

6

30

35

F *

7

30

35

F *

11

30

35

F *

15

30

35

F *

3

40

45

M *

8

40

45

M *

9

40

45

M *

12

40

45

M *

14

40

45

M

 

When used appropriately and accompanied by adequate measures, like restricting access as well as contractual protections, k-anonymization remains an effective technique. In addition to other techniques such as differentially secure algorithms, it assumes a substantial part within the repertoire of privacy-enhancing technologies. The prevalence of big data is leading to an increasing number of publicly available datasets that have the potential to be used for re-identification purposes.

Task 2: Josh and Hannah Case Analysis

Part A

The use of automated big-data driven technology for loan application assessment by banks may provide advantages in terms of enhanced accuracy and impartiality compared to human loan officers. Furthermore, the computer has the capability to make decisions without taking into account personal data which is not relevant to the application for a loan. One potential benefit of using a big data-driven system for evaluating loan applications is the potential for expedited and precise decision-making, along with a potential decrease in applications which are fraudulent (Sun et al., 2014). The software would possess the capability to acquire a diverse array of information sources and employ them in order to construct a more thorough depiction of the applicant. The use of a big data-based system in the assessment of loan applications has the potential to provide outcomes that are characterized by enhanced accuracy and expedited decision-making processes. Additionally, there is potential for a decrease in the quantity of deceitful loan applications. The proposed application would possess the capability to access an extensive array of data sources, enabling the system to amalgamate the information derived from various sources towards a more complete profile regarding the applicant. This information could simplify the bank's loan approval process. The implementation of such an arrangement also has the potential to have favorable social consequences. If the system has the capability to effectively identify applicants with a high risk of default, it has the potential to help to mitigating financial losses resulting from unpaid loans. This step would lead to a reduction in the long-term cost of borrowing funds for all individuals. This would enable the financial institution to make a better-informed decision concerning the approval or rejection of the loan.

Furthermore, the use of such a system may provide considerable societal advantages. For instance, the algorithm's efficacy in accurately identifying applicants with a substantial likelihood of default might potentially contribute to a reduction in defaulted loans (Hassani, Huang, & Silva, 2018). Eventually, this would lead to a reduction in borrowing costs for all individuals.

Part B

Josh and Hannah might have potentially encountered adverse effects as a result of their loan denial, such as the potential scenario where they were declined for a loan which they had the financial means to repay, as well as the likelihood that their rejection was influenced by variables unrelated with their ability to fulfill the loan obligations. Furthermore, it is possible that the algorithm made a decision without adequately considering the individual's specific circumstances. The loan denial may have resulted in financial hardship, damage to their reputation, and mental distress for Josh and Hannah. These harms have significant moral importance. Financial hardship may have significant repercussions since it can lead to several adverse outcomes such as indebtedness, homelessness, including mental health challenges. Another significant consequence is the potential harm to their credibility, that might potentially impede their ability to get financing in subsequent instances. Emotional discomfort is a consequential detriment since it has the potential to precipitate conditions such as anxiety, depression, and several additional disorders of mental health.

The denial of Josh and Hannah's loan request may have resulted in serious ethical damage to them. These potential consequences include potential financial hardship, reputational harm, and emotional anguish (Zimmer, 2020). Economic strain is a significant concern that necessitates attention due to its potential to give rise to many challenges, including but not limited to indebtedness, homelessness, as well as mental health complications. The impairment of their track record is a noteworthy concern, primarily due to its potential to hinder their future loan acquisition prospects. The experience of emotional pain may lead to the development of many mental health conditions, such as anxiety and depression, therefore becoming a notable cause of damage.

Part C

The extensive implementation of this loan evaluation process may have broader societal implications, such as the potential exclusion of individuals who lack the means to repay debts and the potential exclusion of individuals based on non-relevant factors unrelated to their ability to fulfill loan obligations. Furthermore, the system may make decisions without properly considering the user's individual circumstances. The widespread implementation of this loan evaluation process might potentially yield adverse consequences for the broader society, involving the potential exclusion of some demographic groups from accessing loans, the perpetuation of socioeconomic inequities, and the erosion of individual liberties. The denial of credit to certain populations may have significant negative consequences, including financial hardship as well as social marginalization (Kim, Michael Chung, & Paradice, 1997). The maintenance of social disparities is a consequential detriment due to its potential to engender more instances of discrimination and exclusion (Bowern et al., 2006). The decrease of confidentiality rights is a substantial detriment as it might result in the relinquishment of authority over individual data and a curtailment of the capacity to make well-informed choices about its utilization.

The problem of some groups facing challenges in accessing finance is of considerable importance due to its potential to result in financial hardships and social exclusion. Consequently, it is imperative to address this matter. The perpetuation of social inequality is a substantial challenge as it has the potential to engender heightened instances of prejudice and marginalization. The erosion of confidentiality rights is a substantial concern as it leads to a reduced level of autonomy in managing private data and a decreased ability to exert control over its use.

Part D

The two most critical elements in mitigating or averting potential harms resulting from the deployment of this loan evaluation approach are: (1) ensuring the accuracy of the data employed by the system, and (2) incorporating individual circumstances within the assessment process. The mechanism should provide for the possibility of appeals. To mitigate or prevent the aforementioned harms, it is imperative to implement the following three measures or best practices, which are essential for assuring responsibility and safeguarding privacy rights. The significance of enhancing openness lies in its potential to safeguard the fairness and impartiality of the loan review process (Zimmer, 2020). The importance of accountability lies in its potential to facilitate a responsible and ethical loan evaluation process. The preservation of privacy rights has significant importance as it serves to guarantee people the authority to exercise ownership of their private data and make well-informed choices about its utilization.

Conclusion

There are several potential obstacles in the implementation of these regulations including the reluctance of banks and different financial institutions to embrace change, the absence of adequate regulation in this domain, and the complexities associated with guaranteeing adherence to these policies. Nevertheless, it is my contention that these hurdles may be surmounted by a combination of governmental will, societal influence, and economic inducements.

Maintaining openness is of utmost importance as it facilitates the preservation of an objective and equitable loan application process. The presence of accountability is of utmost importance as it serves to facilitate the ethical and responsible execution of the loan review procedure. This is the reason why it is of utmost importance. The safeguarding of confidentiality rights is a crucial factor to consider in order to empower people with autonomy over their private information and enable them to make informed choices about its use. Implementing these measures may provide challenges due to the inherent resistance to change shown by banks alongside other financial institutions, the limited regulatory framework in this domain, and the complexities associated with ensuring compliance with established laws and regulations. However, it is my contention that these challenges may be effectively addressed by the application of sufficient political determination, public advocacy, and monetary incentives.

Although there were instances of favorable outcomes, safeguarding user information continues to be a major concern for corporations. Organizations have recognized the significance of data and have shown its potential in enhancing overall company efficacy. Consequently, enterprises have devised strategies for data analysis to effectively manage the many components of their organization dispersed across global locations. Due to the intrinsic ethical uncertainty surrounding data analytics, it is incumbent upon organizations to allocate a substantial percentage of their resources towards the establishment of approaches and platforms aimed at mitigating any potential negative impact on regulatory norms pertaining to ethical conduct. The organization heavily employs big data analytics, a specialized approach to analyzing vast quantities of data in order to identify patterns, correlations, and gain insights into market dynamics and customer behavior. The reason for this phenomenon is that big data analytics has the capability to expedite the identification of correlations, trends, and insights in a far more efficient manner compared to conventional methodologies. This research aims to address the existing challenges and provide suggestions for future progress. 

References

Bowern, M., Burmeister, O., Gotterbarn, D. and Weckert, J. (2006). ICT Integrity: bringing the ACS code of ethics up to date. Australasian Journal of Information Systems, 13(2). doi:10.3127/ajis.v13i2.50.

Hassani, H., Huang, X. and Silva, E. (2018). Banking with blockchain-ed big data. Journal of Management Analytics, 5(4), pp.256–275. doi:10.1080/23270012.2018.1528900.

George, R. T. (2008). The Ethics of Information Technology and Business. Cambridge: MA: Blackwell Publishing

Kim, C.N., Michael Chung, H. and Paradice, D.B. (1997). Inductive modeling of expert decision making in loan evaluation: a decision strategy perspective. Decision Support Systems, 21(2), pp.83–98. doi:10.1016/s0167-9236(97)00022-5.

Martin, N. and Rice, J. (2011). Cybercrime: Understanding and addressing the concerns of stakeholders. Computers & Security, 30(8), pp.803–814. doi:10.1016/j.cose.2011.07.003.

Niu, B., Li, Q., Zhu, X., Cao, G. and Li, H. (2014). Achieving k-anonymity in privacy-aware location-based services. [online] IEEE Xplore. doi:10.1109/INFOCOM.2014.6848002.

Rogerson, S., Weckert, J. and Simpson, C. (2000). An ethical review of information systems development – The Australian Computer Society’s code of ethics and SSADM. Information Technology & People, [online] 13(2), pp.121–136. doi:10.1108/09593840010339853.

S, K., S, Y. and P, R.V. (2015). An evaluation on big data generalization using k-Anonymity algorithm on cloud. [online] IEEE Xplore. doi:10.1109/ISCO.2015.7282237.

Sinkey, J.F. (1978). Identifying ‘Problem’ Banks: How Do the Banking Authorities Measure A Bank’s Risk Exposure?. Journal of Money, Credit and Banking, 10(2), p.184. doi:10.2307/1991870.

Sun, N., Morris, J.G., Xu, J., Zhu, X. and Xie, M. (2014). iCARE: A framework for big data- based banking customer analytics. IBM Journal of Research and Development, 58(5/6), pp.4:1– 4:9. doi:10.1147/jrd.2014.2337118.

Swinburne.edu.au. (2022). [online] Available at: https://researchbank.swinburne.edu.au/items/64b057c7-44d9-4dd8-aac3-e8456813d941/1/ [Accessed 17 Dec. 2022].

Yaglewad, S. (2020, May 29). Challenges in Business Analytics. What After College. https://whataftercollege.com/business-analytics/challenges-in-business-analytics/

Zimmer, M. (2020). "But the data is already public": on the ethics of research in Facebook. Ethics and Information Technology, 229-241.

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