4DATA001C.2 Statistical Modelling and Analysis BDA B1 4DATA001C.2 Statistical Modelling and Analysis COURSEWORK GROUP B1 ` Lecturer: Ms. Alqa Husni UoW ID: w2120494 IIT ID: 20240376 Name: Marasingha Dheemantha UoW ID: w2120513 IIT ID: 20240888 Name: Wickramaarachchi Athukorala UoW ID: w2120527 IIT ID: 20241159 Name: Dunusinghe Dharmappriya UoW ID: w2120565 IIT ID: 20233061 Name: Yalinda Jayawardena Page 1 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 Table of Contents Part 1 – Data Ethics....................................................................................................... 3 Essay 1 – Marasinghe Dheemantha ............................................................................ 3 Essay 2 – Wickramaarachchi Athukorala ..................................................................... 6 Essay 3 – Dunusinghe Dharmappriya .......................................................................... 9 Essay 4 – Yalinda Jayawardena ................................................................................. 14 Page 2 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 Part 1 – Data Ethics Essay 1 – Marasinghe Dheemantha In the current digital economy, data is a very valuable resource. Data influences social and economic advancement and drives innovation and decision making. But the moral issues surrounding the gathering, handling and sharing of data have grown increasingly difficult. The study of data ethics looks at these issues with an emphasis on the values of justice, openness, responsibility and respect for people's rights. The need to maintain ethical norms grows along with the extent of data use. Data collecting is one of the most critical data ethical challenges. Without complete transparency or informed agreement, organizations gather enormous volumes of personal data via websites, social media, mobile applications and Internet of Things devices. For example, the Cambridge Analytica controversy raised concerns about the use of Facebook user data collected without their agreement to control political outcomes (Cadwalladr & Graham-Harrison, 2018). This incident highlights the need for increased responsibility and informed consent procedures, as well as the ethical dangers of secret data gathering. The problem of individual reporting, in which algorithms analyze data to create comprehensive user profiles, goes hand in hand with this. Personalized recommendations on Netflix or Spotify are an example of how reporting can improve user experience, but it can also result in bias and discrimination. For instance, studies have found that when algorithmic reporting is trained using biased data, it might spread innate social discrimination in loan acceptance or job engaging (O'Neil, 2016). Both the opportunity for appeal of automated outcomes and transparency in algorithmic design should be guaranteed by ethical reporting. Another urgent ethical issue is the effects of data leaks. Large-scale breaks, like the one that hit Equifax in 2017 and impacted over 140 million people, put private data at risk of identity theft, economic loss, and psychological suffering. Data breaches destroy public faith in institutions in addition to causing harm to individuals. Therefore, strict Page 3 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 cybersecurity measures and the data minimization principle which states that only necessary data should be gathered and stored are necessary for ethical data management. Furthermore, there are important ethical issues surrounding the interpretation and publication of data analytics findings. Cherry picking or incorrect data interpretation can mislead the public and result in harmful outcomes. For instance, the misuse of statistical models during the COVID-19 pandemic occasionally led to policy errors or public confusion (Saltelli et al., 2020). Sensationalism must be avoided, technique transparency must be maintained, and doubts must be appropriately communicated. Ethical data practices require legal and regulatory frameworks as fundamental foundations. The Human Rights Act of 1998 and the General Data Protection Regulation (GDPR) are the two most important laws in the UK and the EU. Principles including fairness, justice, transparency, and the right to be forgotten are enshrined in the GDPR, which went into effect in 2018 (European Parliament and Council, 2016). The GDPR establishes strict requirements for gaining consent and get ahead data protection by design. On the other hand the Human Rights Act supports the right to privacy (Article 8), allowing people to have control over their personal information. However, following the law does not ensure moral conduct on its own. Beyond only following to the law, ethical data governance must encourage responsibility within the company culture. This covers open data rules when applicable, ethical review boards for data projects, and encouraging public participation in data governance. To sum up, data ethics is essential to ensuring that technological advancement supports societal norms and human self-respect. Ethical questioning is crucial for everything from data collection and reporting concerns to break consequences and interpreting analytical findings. Although the legal frameworks of the UK and the EU offer a solid basis, states, organizations and individuals must all actively participate in sustainable ethical conduct. Setting ethics as a top priority will be essential to creating a just transparent and trustworthy digital environment since data will continue to be the driving force of the future. Page 4 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 References i. Cadwalladr, C. and Graham-Harrison, E., 2018. Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. [online] The Guardian. Available at: https://www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebookinfluence-us-election [Accessed 22 Apr. 2025]. ii. European Parliament and Council, 2016. Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016R0679 [Accessed 22 Apr. 2025]. iii. Floridi, L. and Taddeo, M., 2016. What is data ethics?. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), p.20160360. Available at: https://doi.org/10.1098/rsta.2016.0360 [Accessed 22 Apr. 2025]. iv. ICO (Information Commissioner's Office), 2023. AI and Data Protection. [online] Available at: https://ico.org.uk/for-organisations/ai/ [Accessed 22 Apr. 2025]. v. O'Neil, C., 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishing Group. vi. Saltelli, A., Bammer, G., Bruno, I., Charters, E., Di Fiore, M., Didier, E., et al., 2020. Five ways to ensure that models serve society: a manifesto. Nature, 582(7813), pp.482–484. Available at: https://doi.org/10.1038/d41586-020-01812-9 [Accessed 22 Apr. 2025]. vii. UK Government, 1998. Human Rights Act 1998. [online] legislation.gov.uk. Available at: https://www.legislation.gov.uk/ukpga/1998/42/contents [Accessed 22 Apr. 2025]. viii. Zuboff, S., 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs. Page 5 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 Essay 2 – Wickramaarachchi Athukorala There has been a very robust debate, in the recent past, about the role social media plays in mental health. Those who support social media contend that it allows people to keep in touch, garner help and speak their minds. They underscore the impact social media has on bringing awareness to serious issues and building excitement for movements. But critics point out the negatives of social media like feelings of loneliness, anxiety, and depression. They question social media platforms being addictive and cyberbullying directly leading to mental distress. An understanding of the balance between gains and sacrifices from using social media is critical to maintaining a healthy relationship with digital age technology. Data collection must be public, legal, and respect personal rights. Legal questions arise when data is obtained without proper consent, when personal data is gathered for vague or unspecified purposes, or when such information is used for purposes other than those for which it was provided. It can lead to biased or harmful outputs, especially if misuse population data, as has been known through unethical data collection. One famous example is the Facebook-Cambridge Analytica scandal, in which millions of user data were harvested without informed consent and used Profiling is the so-called analysis of personal information in order to divide people into categories, with common applications including marketing, risk assessment, and predictive policing. While profiling is sometimes tightly integrated into company practices and policies, improving company strategies and services, or tailoring individual approaches, it appears in some cases as an unfair treatment and in other cases lead to the reinforcement of prejudices, if the profiling is based on bad or unfair data. As an example, algorithms for predictive policing may unduly target minority demographics due to datasets of arrests and criminal activities containing systemic bias against these demographics in historical law enforcement practices (O'Neil, 2016). The risk of bias causes ethical concerns as well as ethical issues stemming from.jobs taking jobs, ethical principles leading to death, a judgmental attitude leading to violence, and so on. Page 6 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 Gain of access or disclosure or stealing of private or personal information without the consent of the user or their information is called as data breach. Such breaches harm the identity theft of financial fraud, reputation loss and emotional distress to those affected. User data must be protected, not just out of ethics, but also as a legal obligation. A case in point is the 2017 Equifax breach that compromised the personal and financial data of 147 million individuals due to substandard cybersecurity controls (Federal Trade Commission, 2019). And this event, sirens blaring, shows the organization peak ethical issue Ethical surveillance implicates data collection and how results are shared and interpreted. Without meaningful accountability, data can easily be distorted or selectively reported, resulting in misleading information for both policymakers and the public. For instance, the use and misuse of infection rate figures throughout the COVID-19 pandemic often played an important role in misinformation and public uncertainty (Ioannidis, 2020). To forestall harm, ethical dissemination demands lucidity, honesty, and transparency, especially when the ultimate verdict will impact the public on health, policy, or social justice. Generally, the main applicable legislation in the EU on this topic is the General Data Protection Regulation (GDPR). The GDPR embodies principles Ⅲ like data minimization, accountability, consent, and individuals' rights to access and rectify their personal information (ICO, 2018). In the United Kingdom these protections are reinforced by the Human Rights Act 1998, which guarantees the right to privacy under Article 8. These legislative frameworks ensure ethical norms are not voluntary, but rather requisite, holding organizations accountable In our data-driven culture, ethical data use sustains human dignity, rights, and autonomy. In simple terms, data ethics promotes fairness, transparency and respect during all stages of data usage, from collection to dissemination. Such ethics are emphasized in legal frameworks, such as GDPR, but proper accountability is as much about the attitude of those involved, from organizations to data scientists, legislators and the public. Lacking a Page 7 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 firm moral basis, data can quickly turn from a source of benefits into a cause of serious public and private damage. References i. Cadwalladr, C. and Graham-Harrison, E., 2018. Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. The Guardian. [online] Available at: https://www.theguardian.com/news/2018/mar/17/cambridgeanalytica-facebook-influence-us-election [Accessed 15 April 2025]. ii. Federal Trade Commission, 2019. Equifax Data Breach Settlement. [online] Available at: https://www.ftc.gov/enforcement/cases-proceedings/refunds/equifaxdata-breach-settlement [Accessed 16 April 2025]. iii. Information Commissioner’s Office (ICO), 2018. Guide to the General Data Protection Regulation (GDPR). [online] Available at: https://ico.org.uk/ [Accessed 19 April 2025]. iv. Ioannidis, J.P.A., 2020. Coronavirus disease 2019: the harms of exaggerated information and non-evidence-based measures. European Journal of Clinical Investigation, 50(4), p.e13222. [online] Available at: https://onlinelibrary.wiley.com/doi/full/10.1111/eci.13222 [Accessed 19 April 2025]. v. O’Neil, C., 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishing. [Accessed 18 April 2025]. vi. The UK Human Rights Act (2023) Pinsent Masons. Available at: https://www.pinsentmasons.com/out-law/guides/uk-human-rights-act (Accessed: 20 April 2025). Page 8 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 Essay 3 – Dunusinghe Dharmappriya Data ethics refers to the moral principles that guide the collection, use, analysis, and dissemination of data. As defined in the lecture material, it is “concerned with the principles of right and wrong behavior” in relation to data (Worrall, 2020). Data ethics is moral principles that govern a person's behavior, or the conducting of an activity concerned with the principles of right and wrong behavior. Data ethics is essential to ensuring that analytical procedures are fair, accountable, and considerate of individual rights in a time when decision-making based on data has become commonplace. To collect data, we need to be able to define the goal of our research and what we are trying to learn from it. For example, if we need to find out student satisfaction with online learning, we can generate a survey with a list of questions, like their age, what they are studying, etc. The rise of data ethics began with the increase in data collected from surveys over the years. People demanded to know what was happening to the data collected and whether they were secure or not. People questioned the transparency, accountability, privacy, and security of their data. With the collection of personal information in vast amounts, people's privacy concerns have begun to grow. As the solution to these issues, we could increase the transparency of data to avoid the misuse of data. With transparency, people will be able to understand what was happening to their data and where the data ended up. Because of this, people will be able to trust surveys and not be feared to attend surveys. And it solves many data collection problems modern collectors face. We can solve more issues relating to data collection if we follow data ethics in every possible way Individual profiling is “any form of automated processing of personal data consisting of the use of personal data to evaluate certain aspects relating to a natural person, in particular, to analyze or predict aspects concerning that natural person’s performance at work, economic situation, health, personal preferences, interests, reliability, behavior location or movements” Profiling consists of using an individual's data to analyze and predict their Page 9 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 behavior, such as determining peplos performance at work, financial situation, health, preferences, lifestyle, etc. With individual profiling, people may face problems such as having their personas created without their knowledge, being scammed through stolen identities, being influenced or manipulated into certain actions, and losing the freedom to make their own choices. Ethical data use demands that scammers and individuals who misuse data must not exploit vulnerabilities or manipulate others without their awareness. Data profiling requires huge pools of data to ensure the accuracy of results. When large companies gain a majority share in a market, they gain a large consumer base and therefore also gain a monopoly on consumer data. This allows them to conduct data profiling more successfully, boosting their performance which further increases their power as a dominant market player. If the European Competition Authorities perceive this as anti-competitive behavior, such companies could face potential sanctions. A data breach is any security incident in which unauthorized parties access sensitive or confidential information, including personal data (Social Security numbers, bank account numbers, healthcare data) and corporate data (customer records, intellectual property, financial information). When a data breach happens, areas like privacy, trust, and Societal Consequences are heavily impacted. When we look at the privacy area, data breaches expose all of our personal and sensitive data without consent. This can result in identity theft, financial fraud, and other forms of personal harm. For the trust area, once their data is breached to dark websites or in the hands of unethical hackers, they lose their customers’ trust. Even small incidents can significantly damage relationships, and customers less likely to engage in future online activities. Because of this long-term loss of trust, This long-term loss of trust may hinder digital participation and slow down innovation in major companies. For example, Microsoft had a huge data breach in 2021 and it impacted 30000 companies in the United States and 60000 companies worldwide because they followed ethics they were able to reduce the harm caused by hackers, and for a local example, we can tell Page 10 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 which happened recently in Cargils Bank and it breached a lot of sensitive data from both customers and employees. Data dissemination is the process of making data accessible to users, while interpretation of results involves understanding and drawing conclusions from the data. Dissemination ensures the data is shared widely, while interpretation provides meaning and insights. Ethical dissemination requires sharing all results positive, negative, or inconclusive to avoid publication bias and misinforming stakeholders. Transparency about methods, limitations, and uncertainties is essential to prevent misleading interpretations Results should be disseminated promptly to maximize their benefit, especially in urgent contexts like public health emergencies. Making key information publicly available, including protocols and analysis plans, supports transparency and reproducibility. For example After surveying student satisfaction at a university, the results are published in a report, shared through email, and presented at a meeting for the academic staff and student union. That’s data dissemination. From the student satisfaction survey, 80% of students say they’re happy with the library services, but only 40% are satisfied with online learning platforms. That helps us to get the interpretation result. The Human Rights Act 1998(HRA) and the UK/EU legal framework play an important role in shaping data ethics by establishing legal boundaries, safeguarding individual rights, and promoting responsible data practices. In the Human Rights Act 1998, article 8 shows that HRA guarantees the right to respect for private and family life, home, and correspondence. This directly ties to data ethics by requiring researchers and organizations to minimize intrusions into personal privacy when collecting or sharing data. When we look at the legal framework, in 2018, GDPR and DPA came from the backbone of data ethics and enhanced the protection of sensitive data and automated decisionmaking. Every phase of the data lifecycle must consider ethical issues because data continues to influence modern society. The examples given demonstrate how unfairness, bias, and a loss of autonomy can result from profiling, biased algorithms, inadequate data procedures, Page 11 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 and a lack of transparency. Respecting the values of justice, human rights, and privacy is not just required by law; it is also a social need. Organizations can utilize data ethically and responsibly for the sake of all by adopting strategies like anonymization and transparency and coordinating their operations with the GDPR and the Human Rights Act. References i. Bidault, P.-E. (2022) Profiling. https://www.dastra.eu/en/guide/definition-profilinggdpr/56328 (Accessed: April 18, 2025). ii. Blog, D.P.B. (2020a) 'The legal ethics of data profiling,' Durham Pro Bono Blog, 3 March. https://www.durhamprobonoblog.co.uk/post/the-legal-ethics-of-data-profiling (Accessed: April 18, 2025). iii. Blog, D.P.B. (2020b) 'The legal ethics of data profiling,' Durham Pro Bono Blog, 3 March. https://www.durhamprobonoblog.co.uk/post/the-legal-ethics-of-data-profiling (Accessed: April 18, 2025). iv. Chin, K. (2024) Biggest data breaches in US history (Updated 2025). https://www.upguard.com/blog/biggest-data-breaches-us (Accessed: April 19, 2025). v. Data Collection: Process & Challenges | SafetyCulture (2024). https://safetyculture.com/topics/data-collection/#common-challenges (Accessed: April 18, 2025). vi. Directory, S. (2024) Data Breaches: ethical implications? → Question. https://sustainability-directory.com/question/data-breaches-ethical-implications/ (Accessed: April 19, 2025). vii. Dissemination of research (no date). https://unisa.edu.au/research/integrity/responsibleresearch-practice/dissemination-of-research/ (Accessed: April 19, 2025). viii. Government Digital Service (2025) Data protection. https://www.gov.uk/dataprotection (Accessed: April 18, 2025). Page 12 of 17 4DATA001C.2 Statistical Modelling and Analysis ix. Jayan, J. (2024) BDA B1 Importance of ethical data collection. https://www.promptcloud.com/blog/importance-of-ethical-data-collection/ (Accessed: April 18, 2025). x. Participation, E. (no date) Human Rights Act 1998. https://www.legislation.gov.uk/ukpga/1998/42/contents (Accessed: April 18, 2025). xi. Privacy Analytics (2024) Privacy Analytics - Understanding Re-identification Risk when Linking Multiple Datasets. https://privacy- analytics.com/resources/articles/managing-re-identification-risk-when-linking-multipledatasets/ (Accessed: April 18, 2025). xii. Ravinetto, R. and Singh, J.A. (2022) 'Responsible dissemination of health and Medical Research: Some guidance points,' BMJ Evidence-based Medicine, 28(3), pp. 144–147. https://doi.org/10.1136/bmjebm-2022-111967. xiii. UK Data Service (2021) Human Rights Act - UK Data Service. https://ukdataservice.ac.uk/learning-hub/research-data-management/dataprotection/data-protection-legislation/human-rights-act/ 2025). Page 13 of 17 (Accessed: April 19, 4DATA001C.2 Statistical Modelling and Analysis BDA B1 Essay 4 – Yalinda Jayawardena Data can be used for insightful analysis and decision-making. Yet, this powerful resource challenges how organisations collect, store, and use data ethically. Therefore, these organisations must uphold a certain level of ethics that relate to data. Data ethics are moral obligations of gathering, protecting, and using personally identifiable information, and they affect individuals. “Data ethics asks, ‘Is this the right thing to do?’ and ‘Can we do better?” (Harvard Business School, 2021). Issues related to data collection. “Ethics in data collection, also known as what data ethics is, refer to the principles that businesses should follow when it comes to how they collect, use, and protect data from any number of sources.” (University of the Cumberlands, 2024). Data transparency is crucial for data ethics, the ability for the customer to understand what kind of data is being collected, how it is collected, and how it is potentially being used. Transparency in data collection is critical to building customer trust and preventing the misuse of information after it is collected. Data privacy and consent often go together because if a person provides their information to a business, they assume the data will be kept secure. Therefore, data privacy means that the data will be protected from public exposure. Further, lack of relevance is an issue related to data collection. Organisations should ensure that data collection spans the relevant demographics. But, if they aren’t going to contribute to the relevant study, they should be avoided. Individual profiling, is defined under the general data protection regulation as, “any form of automated processing of personal data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person, in particular to analyse or predict aspects concerning that natural person’s performance at work, economic situation, health, personal preferences, interests, reliability, behaviour, location or movements” (Ghant University, 2024). Legal profiling issues are as follows: e-commerce companies can create data profiles of customers based on their search and purchase history for targeted advertising. Some customers argue that it is an invasion of their privacy. Article 22(1) of the EU, GDPA restricts Page 14 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 organizations from making solely automated decisions. Furthermore, under Article 17 of the GDPR, all personal data must be removed immediately when they are no longer needed for its original purpose. In addition, Facebook – Cambridge Analytica (CA) data scandal highlights the dangers of using data profiling in political advertising. In 2018, it was revealed that Cambridge Analytica harvested millions of personal data from Facebook profiles without consent and used it for political advertising purposes. Which triggered calls for higher regulation of tech companies’ use of personal data. A data breach is any security incident related to unauthorised parties' access to sensitive or confidential information such as personal data (Social Security numbers, bank account numbers, healthcare data). Often, the term “Data breach” is їnterchangable with “cyberattacks”. According to the IBM cost of data breach report, the average global cost of a data breach is USD 4.88 million. At the same time, organisations of every type and size are vulnerable to breaches, but the severity and cost could vary. For example, the average cost of breaches in the USA is 9.36 million, about 4 times that of India (USD 2.35 million). In 2007, the TJX Corporation, the parent company of TJ Maxx and Marshalls, recorded the largest and the costliest data breach in US history. More than 94 million customer records were compromised and the company suffered more than 256 million in financial losses. In 2013, Yahoo suffered a data breach where hackers exploited the weakness of the company’s cookie system to access the names, birthdays, email addresses, and passwords of all 3 billion Yahoo users. Data dissemination is the targeted distribution of information and intervention materials to a specific audience. For instance, if we take the effective Health Care Program to facilitate the use of Health-Related Evidence, these strategies are used to spread evidence-based knowledge interventions across geographic locations, practice settings, or social or other networks of end-users like patients and health care providers. Page 15 of 17 4DATA001C.2 Statistical Modelling and Analysis BDA B1 Data interpretation involves making sense of numerical, textual, and visual data. It consists in analysing, evaluating, and drawing conclusions from data sets for decision-making. It is important because it helps to identify trends, forecast future outcomes, and make informed decisions. Key data interpretation techniques are descriptive statistics, inferential statistics, and data visualisation. The legal framework plays a crutial role when it comes to enforcing rights and responsibilities when handling data. It makes organisations accountable for their conduct, therefore encouraging them to be ethical when handling data. Article 8 of the Human Rights Act, the Data Protection Act, and the GDPR are examples of the legal framework. Several approaches can be made when following the legal framework for ethical data usage. This includes the steps of recognising the issue, obtaining the facts, evaluating possible actions, deciding on the course of action, and reflecting on the decision. In addition, two more techniques can be followed when handling data. Data anonymisation and the POTOMAC guidelines. In conclusion, the use of data is highly beneficial for organisations. However, it is the responsibility of the organisations and businesses to take ethical considerations when handling data, safeguarding the individuals, and being authentic. References i. Cheong (2020) 'The Legal Ethics of Data Profiling,' Durham Pro Bono Blog, 3 March. https://www.durhamprobonoblog.co.uk/post/the-legal-ethics-of-data-profiling (Accessed: April 14, 2025). ii. Communication and Dissemination Strategies To Facilitate the Use of HealthRelated Evidence (2012) Agency for Healthcare Research and Quality. https://effectivehealthcare.ahrq.gov/products/medical-evidencecommunication/research-protocol (Accessed: April 14, 2025). Page 16 of 17 4DATA001C.2 Statistical Modelling and Analysis iii. Cote, C. (2021) 5 Principles BDA B1 of data Ethics for business. https://online.hbs.edu/blog/post/data-ethics (Accessed: April 14, 2025). iv. Directorate-General for Communication (no date) Legal framework of EU data protection. https://commission.europa.eu/law/law-topic/data-protection/legal- framework-eu-data-protection_en (Accessed: April 14, 2025). v. Ghent University (2024) GDPR: when do I engage in 'profiling'; what should I think about? And what is 'exclusively automated individual decision-making'? https://onderzoektips.ugent.be/en/tips/00002169/# (Accessed: April 14, 2025). vi. Hulatt, L. (2022) Ethical Issues In Data Collection. https://www.studysmarter.co.uk/explanations/english/research-and-composition/ethicalissues-in-data-collection/ (Accessed: April 14, 2025). vii. Kosinski, M. (2024) What is a data breach? https://www.ibm.com/think/topics/databreach (Accessed: April 14, 2025). viii. Mildebrath, H. (2025) Understanding EU data protection policy, European Parliament. PE 698.898. European Parliament. https://www.europarl.europa.eu/RegData/etudes/BRIE/2022/698898/EPRS_BRI(202 2)698898_EN.pdf (Accessed: April 14, 2025). ix. Nawaz, S. (2024) Mastering Data Interpretation: Techniques, Insights, and Strategies. https://www.linkedin.com/pulse/mastering-data-interpretation- techniques-insights-strategies-nawaz-wkjyf (Accessed: April 14, 2025). x. Parliament of the UK (2019) The Right to Privacy (Article 8) and the Digital Revolution. https://publications.parliament.uk/pa/jt201919/jtselect/jtrights/122/12204.htm (Accessed: April 14, 2025). xi. Shelley, S. (2024) Understanding the Ethics of Data Collection and Responsible Data Usage. https://www.ucumberlands.edu/blog/understanding-the-ethics-of-data- collection (Accessed: April 14, 2025). Page 17 of 17
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