BUSA8030 Chap.12 Data Ethics, Privacy and Governance
Data Ethics, Privacy and Governance
Week 9 opens the ethics and governance arc by pairing every new data capability with a matching concern. Smart advertising can wound people, as when baby products are shown after a stillbirth. Automated hiring can embed bias against applicants seen as foreign. Real-time supply-chain traceability can become sensitive in contexts such as war, and robotics can de-skill a workforce.
Corporate use of big data concerns governments, industry, academia, NGOs, businesses and citizens, and their key issues are ethics, security, privacy and data governance, meaning the legal aspects of acquiring, storing and using data.
Stakeholders see these issues from different positions.
Industry sets the tone for data practice, organisations define processes and guidelines, individual decision-makers use the data, customers may not know what their data is used for, and government writes and enforces the laws.
Ethical issues are multi-layered, involving customers, managers, data scientists, regulators and the firm itself, which is why the lecture calls for a whole-of-organisation response in which all staff share responsibility.
Privacy becomes concrete through consent.
The lecture lists questions consumers cannot easily answer: who collects data, how, for what purpose, what is done with it, whether it is shared, whether consent was given, who owns it and whether any policy exists. One view treats data collection as a fair exchange for free services; the other stresses that consent is ambiguous because people rarely read or understand agreements.
Governments increasingly turn ethics into law, most influentially through the European Union's General Data Protection Regulation of 2018. The chapter closes with a case of an advertising algorithm that ignored grief, analysed through accountability, responsibility and fairness.
What this chapter covers
- 01
Possibilities and concerns in four functions
- 02
Ethics, security, privacy and governance
- 03
Stakeholders and multi-layered responsibility
- 04
Consent questions and two views of privacy
- 05
From ethics to law
- 06
Designing for accountability and fairness
Worked example · free
AskSia practice weighting: an ethics review of automated hiring
- 1Risk one: past hiring bias is learnt, disadvantaging some groups, as in the lecture's hiring example.
- 1Risk two: applicants do not know how their data is used, so consent is ambiguous.
- 1Safeguard: audit outcomes by group before and after deployment.
- 1Safeguard: tell applicants how screening works and offer a human review on request.
- 1Governance: name an accountable owner and record decisions about data sources.
Key terms
- Data governance
- The policies and legal aspects of acquiring, storing and using data.
- Informed consent
- Agreement to data use given with a real understanding of what will happen to the data.
- Quid pro quo
- An exchange view in which users trade personal data for free services.
- Algorithmic bias
- Systematic unfairness produced when a model learns from skewed or prejudiced data.
- Accountability
- The obligation of a named person or body to answer for outcomes.
- Surveillance capitalism
- A term for business models built on collecting and monetising behavioural data.
Data Ethics, Privacy and Governance FAQ
What four concerns does big data raise according to Week 9?
Ethics, security, privacy and data governance. Governance is defined as the legal aspects of acquiring, storing and using data, and managers must understand all four to avoid scandals and legal consequences.
Who is responsible for data ethics in an organisation?
The lecture shows responsibility spread across industry, the organisation, individual decision-makers, data scientists and regulators, and calls for a whole-of-organisation response in which all staff share ongoing responsibility.
What is the problem with online consent agreements?
People rarely read or understand them, so consent is often not genuinely informed. That ambiguity sits against the quid pro quo view that data is a fair price for free services.
Why is the GDPR important outside Europe?
The regulation, in force since 2018, imposes obligations on organisations anywhere if they target or collect data about people in the European Union, so global firms must comply.
How can an organisation show it takes data ethics seriously?
Week 9 cites Dalal and Pauleen: analytics maturity shows in how well an organisation secures and respectfully uses data from customers, employees and partners, through real policies and practices.
Assessment move
For the critical analysis report, choose one data practice in your organisation and run it through the four concerns: ethics, security, privacy and governance. A focused analysis of one practice is stronger than a list of generic risks, and it gives the ethics section real evidence.
In the group plan, include an ethics and privacy section that answers the consent questions for your proposed solution.
Who collects the data, for what purpose, with what consent and who is accountable? Answering these upfront shows responsible design.
For the diary, reflect on whether ethics should sit inside law or law inside ethics, using an example from class. The lecture poses this deliberately as an open question.
Working through Data Ethics, Privacy and Governance in BUSA8030? Sia is AskSia’s AI Management tutor — ask any BUSA8030 Data Ethics, Privacy and Governance question and get a clear, step-by-step explanation grounded in how BUSA8030 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
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