A financial institution plans to implement an AI-powered credit scoring system to evaluate loan applications based on applicants' income, employment history, repayment records, and other financial information. The institution believes that AI can improve decision-making accuracy, reduce processing time, and minimise human bias. However, concerns arise when several applicants with similar financial profiles receive different credit scores, particularly those from lower-income communities or certain demographic groups. Further investigation suggests that historical lending data and some variables used by the AI system may contribute to unfair outcomes.

Critically discuss the ethical implications, fairness, and potential biases associated with implementing this AI-powered credit scoring system. In your discussion, explain how AI is used in credit scoring and why fairness is important, identify and illustrate TWO types of bias, examine the consequences for individuals, financial institutions, and society, and evaluate TWO strategies for mitigating bias, including ONE limitation of each strategy. Support your arguments with relevant examples.

An international technology company develops an AI-powered recruitment system to screen job applications, evaluate candidates, and recommend the most suitable individuals for employment. The system is trained using historical recruitment records and achieves an overall prediction accuracy of 92%. However, after several months of operation, the company discovers that the AI consistently recommends more male candidates than female candidates and gives lower scores to applicants from certain universities and geographical regions. Interestingly, the developers claim that the system is unbiased because gender, ethnicity, and nationality were never explicitly included in its decision-making criteria.

Critically discuss how an AI system can become biased even when it is designed to make objective decisions. In your discussion, identify and explain THREE possible sources of AI bias, evaluate whether high prediction accuracy guarantees fairness, examine the ethical and societal consequences of biased AI decisions, and propose TWO approaches to reducing bias, including their limitations. Finally, discuss whether AI can ever be completely free from bias. Support your arguments with relevant examples.

A hospital plans to implement an AI-powered system to diagnose diseases, recommend treatments, and prioritise patients based on medical urgency and available resources. One day, the system must decide between two patients competing for the last available ICU bed: a critically ill patient from a low-income family requiring immediate treatment and a wealthy patient with a less urgent condition who is willing to pay significantly more for priority access. Hospital management argues that prioritising the wealthy patient could generate additional revenue to improve hospital services.

Critically discuss the technical feasibility, ethical implications, and societal impact of implementing such an AI system. How should the system prioritise patients, and who should be accountable for its decisions? Support your arguments using the CLAIM structure.

A university introduces an AI system that predicts which first-year students are likely to drop out, using their attendance, online learning activity, fee payments and background data, and alerts advisors to contact the students flagged as at risk. In the pilot, dropout fell from 17% to 14%, but the system flagged far more students than the advisors could support, was more likely to flag students from certain nationalities, and informed students about their risk status without clear consent. As an AI specialist, critically discuss the technological strengths and limitations of this system, its ethical implications (such as fairness, privacy and transparency), and its feasibility in practice. Justify your evaluation through logical reasoning and relevant real-world examples, and recommend whether the university should continue, modify, or stop using the system.