Attempts the questions below:
2 marks questions
- What are the TWO main features of Rule based decision making tools?
- What are the TWO main characteristics of AI-based decision making tools?
- Name TWO examples where Rule based decision making tools are used.
- Name TWO examples where AI based decision making tools are used.
- Name TWO primary challenges and limitations of rule-based systems in modern applications?
- Recommend TWO best practices for the effective implementation of rule-based systems.
- Describe ONE function that Dutch SYRI system was designed to do
- What is a data-driven approach to screening applicants?
- What are the two types of decision-making tools mentioned in the document?
- Define "supervised learning" as described in the document.
- Identify three stakeholders involved in the screening process.
- What are some of the ethical concerns related to AI-based screening tools?
- What role does a domain expert play in rule-based decision-making?
- How does a decision tree relate to rule-based decision-making tools?
4 & 6 marks questions (Describe & explain
- Distinguish between supervised machine learning and unsupervised machine learning.
- Describe how a rule-based decision-making tool works.
- Describe the purpose of using AI in applicant screening.
- Describe the difference between qualitative and quantitative data in the screening process.
- Describe how supervised learning is applied in AI-based screening tools.
- Describe the main challenges organizations face when using traditional screening methods.
8 marks questions
- Using examples from the provided case study and your own knowledge, evaluate the effectiveness of rule-based systems compared to AI-based systems in enhancing personalized learning experiences in education.
- Evaluate the ethical implications of using AI for applicant screening.
- Evaluate whether AI-based screening tools can ensure fairness in recruitment.
- Evaluate the risks and benefits of using a rule-based decision-making tool compared to an AI-based tool.
- Evaluate the role of human oversight in AI-driven applicant screening.
- Evaluate the potential for discrimination in data-driven screening methods.
- Evaluate how transparency in screening algorithms can build or erode trust among applicants.
- Evaluate the effectiveness of supervised learning in minimizing bias in applicant screening.
- Evaluate the long-term impact of automated screening tools on workplace diversity.
12 mark questions
- Using the provided stimulus material and your own research, evaluate four real-life rule-based decision-making tools that have generated more concerns than benefits in the screening and selection of applicants for specific services. [12]
- Using examples from the sample and your own knowledge, evaluate the impact of rule-based decision-making tools compared to artificial intelligence (AI) decision-making tools in promoting ethical decision-making in the healthcare industry. [12]
- To what extent are rule-based systems and AI-based systems effective in supporting medical decision-making in rural communities? [12]
- Using information from the prerelease material and your own knowledge, evaluate the impact of AI-based decision-making tools compared to rule-based decision-making tools in promoting ethical decision-making in choosing future university students. [12]
- Using examples from research and your own knowledge, to what extent has use and integration of AI decision making tools in job application outweighed the use of Rule based decision making tools? [12]
