Overview
Qualification Grading Type
Graded
Graded
Be able to differentiate between ethical and unethical behaviour in computing.
Describe the principles of ethical behaviour in computing.
Description could include a set of moral guidelines, behaviours or code of conduct of how people should use computer technology and the ten commandments of computer ethics.
Analyse the impact of computer technology on issues of:
Discussions could include reference to data protection, collection and storing of personal information, GDPR, network protection, hacking, cyber security, responsibilities of the individual, Data Privacy Act and Computer Misuse Act.
Explain examples of unethical behaviour in computing and what is being done to minimise or stop it.
Provide at least three examples of unethical behaviour. Could consider cyberbullying, trolling, software piracy, phishing, employees’ use of equipment, leaking of sensitive information, abuse of privileged access, the distribution of misinformation or limiting access to information and freedom of information.
Know the key social issues in computing.
Discuss the key social issues in computing:
Consider both positive and negative issues where applicable.
Explain the concept of Green Computing and the strategies which help its implementation.
Consider energy consumption, environmental waste, consumables, hazardous substances, life cycle of hardware. Consider what manufacturers can do, what businesses and organisations can do and what individuals can do.
Understand how intellectual property rights affect the computing industry.
Using software and/or hardware examples, explain the three main types of intellectual property rights.
Include discussions on the use of patents, copyright and trademarks in the context of software and/or hardware.
Understand the ethical considerations of emerging technology.
Describe the ethical considerations of emerging technologies for individuals, businesses and society.
Consider the rise of artificial intelligence, virtual reality, wearable technologies, big data, activity monitoring and machine learning.