Disruptive effects, ethics and emerging AI in intelligent systems: HSC Enterprise Computing Intelligent Systems
“Investigate the disruptive effects of intelligent systems, including multitasking versus digital distraction, working differently to complete the same task, automation of enterprise and manufacturing processes and impact of artificial intelligence (AI) on employment; investigate social and ethical issues to be considered when developing, implementing and using intelligent systems; investigate current and emerging technologies associated with intelligent systems, including AI; explain how intelligent systems combine innovative techniques and technologies to meet the needs of enterprise”
Intelligent systems disrupt work through multitasking and distraction, new ways of doing the same tasks, automation, and AI's mixed effects on employment. Ethical development and use requires fairness, transparency, accountability, privacy, safety and human oversight. Enterprises combine AI, machine learning, IoT, robotics and expert systems to meet needs such as efficiency, accuracy and personalisation.
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What this dot point is asking
This is the impacts section of Intelligent Systems. You need to investigate four disruptive effects named in the syllabus, discuss social and ethical issues across developing, implementing and using intelligent systems, know current and emerging technologies (especially AI), and explain how enterprises combine techniques to meet their needs.
The answer
Disruptive effects
- Multitasking versus digital distraction: intelligent tools let people juggle more (smart inboxes, assistants that schedule meetings), but constant notifications and task switching reduce focus, raise stress and can lower the quality of work.
- Working differently to complete the same task: the goal stays the same but the method changes (doctors use AI image analysis to help read scans; journalists use AI transcription; warehouse staff pick orders guided by algorithms).
- Automation of enterprise and manufacturing processes: robots, robotic process automation and AI perform repetitive physical and administrative tasks, increasing speed, consistency and safety, and reducing costs.
- Impact of AI on employment: some tasks and roles are displaced (routine clerical and assembly work), many roles are transformed (humans supervising and working with AI), and new roles are created (AI trainers, data engineers). Workers need reskilling, and benefits and costs are unevenly shared.
Social and ethical issues
Across the life cycle of an intelligent system:
- Developing: biased or unrepresentative training data, privacy of data used to train, intellectual property in training data, and testing for safety.
- Implementing: consultation with affected workers and users, transparency about where AI is used, security, and plans for job transitions.
- Using: accountability when AI causes harm, explainability of decisions, human oversight of high-stakes decisions, over-reliance on automation, surveillance, and accessibility and digital divide.
Is it fair? Is it transparent and explainable? Who is accountable? Is privacy protected? Is it safe and reliable? Does it benefit the people it affects?
Current and emerging technologies
- Generative AI: large language models and image, audio and code generators.
- Computer vision: facial recognition, quality inspection, medical imaging.
- Autonomous vehicles and drones.
- Robotics and cobots (collaborative robots working safely beside people).
- Edge AI: running models on devices for speed and privacy.
- Digital twins: virtual models of physical systems for simulation.
- Natural language processing: chatbots, translation, voice assistants.
Combining techniques to meet the needs of enterprise
Enterprises rarely use one technique alone. For example, an online retailer combines IoT sensors in warehouses, machine learning demand forecasts, expert-system rules for fraud checks, robotics for picking, and a chatbot for customer service. Each technique covers a need (efficiency, accuracy, availability, personalisation), and together they create a responsive, data-driven enterprise.
A hospital introduces AI to help read chest X-rays.
- Working differently: radiologists review AI-highlighted areas first, reducing time per scan.
- Employment: radiologists are not replaced but their work shifts to complex cases and checking AI output.
- Ethics: the AI was trained mostly on adult scans, so it is less accurate for children (bias); the hospital restricts its use to adults and keeps a radiologist as the final decision maker (accountability, human oversight).
- Combination: the AI model, the hospital's image storage system and a decision support dashboard work together to prioritise urgent cases.
- Only discussing job losses
- Also cover changed and new jobs, and who benefits.
- Listing ethical issues without a stage or scenario
- Link each issue to developing, implementing or using the system.
- Treating AI as one technology
- Name specific techniques and how they combine.
Practice questions
Original practice questions graded from foundation to exam level, each with a full worked solution. Try them before revealing the solution.
foundation3 marksOutline one positive and two negative disruptive effects of automation in manufacturing.Show worked solution →
- Positive: robots work continuously with consistent quality, lowering costs and removing people from dangerous tasks.
- Negative: routine assembly jobs are lost, affecting workers without retraining opportunities.
- Negative: factories become dependent on complex systems, so faults or cyberattacks can halt production.
Marking guide: 1 mark each.
core4 marksExplain two ethical issues a company must consider before implementing an AI system that screens job applications.Show worked solution →
Bias and fairness. If the AI learns from past hiring decisions, it may repeat discrimination (for example against women or people from certain postcodes). The company must test outcomes across groups and remove biased features.
Transparency and accountability. Applicants rejected by the AI deserve to know how decisions were made and to challenge them. The company should be able to explain the main factors, keep a human reviewer for final decisions and remain accountable for outcomes.
Marking guide: 2 marks per issue explained in context.
exam7 marksA national bank plans to combine a chatbot, machine learning fraud detection and robotic process automation to handle customer enquiries and back-office processing. Explain how these intelligent systems combine to meet the bank's needs, and evaluate the disruptive effects and ethical issues.Show worked solution →
- How they combine
- The chatbot uses natural language processing to answer routine questions 24 hours a day and passes complex cases to staff. The fraud model scores transactions in real time and blocks suspicious ones, alerting customers through the chatbot. Robotic process automation completes rule-based back-office tasks (updating addresses, processing forms). Together they cut costs, speed up service and improve security.
- Disruptive effects
- Call centre and back-office roles shrink, while demand grows for staff who handle complex cases, supervise AI and analyse data. Staff work differently, spending less time on data entry. Customers get faster service but may find it harder to reach a person.
- Ethical issues
- Fraud models may wrongly block legitimate customers, with some groups flagged more often (bias). Customers should know when they are talking to a bot (transparency). Conversation and transaction data must be protected (privacy). The bank remains accountable for automated decisions and must provide human review and appeal.
- Evaluation
- The combination meets the bank's needs for efficiency and security, but its success depends on managing job transitions fairly, monitoring for bias and keeping accessible human support, especially for vulnerable customers.
Marking guide: 2 marks for how systems combine, 2 marks for disruptive effects, 2 marks for ethical issues, 1 mark for a justified evaluation.