23-Ind-B4 Design of Information Systems · December 2017
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
National Exams — December 2017 — 98-Ind-B4, Design of Information Systems. 3 hours; closed book, no calculator permitted. The exam comprises four parts: Part A (select 20 terms from the list given and explain each in a sentence or two, no more than 50 words, 2 marks each = 40 marks), Parts B and C (select 2 of 5 questions in each part, 11 marks each = 22 marks per part), and Part D (select 1 of 2 questions, 16 marks). Complete answers to every term and every question in all four parts follow below, not only the minimum selection a candidate would submit on exam day.
Reference texts: Laudon & Laudon, Management Information Systems: Managing the Digital Firm, 15th ed.; Schwalbe, Information Technology Project Management, 9th ed.
Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.
An auto or health insurer offers a discount to customers who agree to install a telematics device (auto) or share fitness-tracker data (health), then uses the resulting Big Data (Question 1, term 6) — driving speed, braking patterns, location, or step counts and heart-rate data — to price each individual's premium based on their own measured behaviour rather than the actuarial average for their demographic group.
Privacy and information rights (Question 1, term 22): the data collected is extremely granular and reveals far more than the insurer strictly needs for pricing — a customer's exact daily routes and schedule, or detailed health patterns, become visible to a third party who was not the original intended recipient of that information. Consent that is not genuinely voluntary: the "discount" framing means declining to share data carries a real financial penalty, so participation is not freely chosen in the way informed consent is meant to require. Fairness and potential discrimination: behaviour-based pricing can systematically disadvantage people whose circumstances, not their driving/health choices, drive the pattern the algorithm penalizes (e.g., someone whose job requires late-night driving through higher-risk areas, or a chronic health condition reflected in fitness-tracker metrics), effectively re-introducing discrimination the traditional actuarial model did not explicitly encode. Function creep and secondary use: data collected for pricing can, without further explicit consent, be repurposed for other analytics, sold to third parties, or used in ways the customer never anticipated when they agreed to the original discount.
1. Identify and describe the facts clearly. What data is actually collected, by whom, how it is used, retained, and who else can access it — without this factual grounding, the ethical questions cannot be evaluated concretely. 2. Define the conflict and identify the higher-order values at stake. Here, the insurer's/society's interest in accurate risk pricing (and the honest customers who benefit from it) conflicts with the individual's right to privacy and to be free of a surveillance-conditioned discount. 3. Identify the stakeholders. Customers who opt in, customers who decline and may pay relatively more, the insurer, regulators, and any third party the data might be shared with each have distinct, sometimes opposing interests. 4. Identify the options reasonably available, and evaluate the risks, benefits, and moral costs of each. Options might range from opt-in telematics pricing as currently designed, to a version with stricter data-minimization and no third-party sharing, to declining to offer behaviour-based pricing at all — each with a different balance of pricing accuracy, individual privacy, and fairness across the customer base, which the organization must weigh and be prepared to defend.
