1. Discuss four (4) example applications that show the consequences to use data with poor quality to train machine learning algorithms. Provide evidence. (12 marks) 2. Machine learning starting to replace human in decision making in important situations such as: whose loan got approach and which candidate is suitable for a certain job vacancy. Nonetheless, machine learning models can perpetuate societal bias. Designers of machine learning systems have a moral responsibility to ensure that their systems are in fact fair. Identify four (4) applications of Al where the fairness and bias can be an issue that needs to be solved and analyze the situations. Provide evidence. (12 marks) 3. Despite many positive aspects of artificial intelligence inventions, they too have unintended negative side effects. Recognize and outline the four (4) negative side effect of Al technologies with examples. (12 marks) 4. Classify situations when explainable Al is necessary or unnecessary. Identity the reasons why explainable Al is important. (5 marks) 5. It is one challenge to make an Al system accurate, fair, safe, and secure. Explain three (3) ways how to make people trust the system they use. (9 marks)