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SECTION B (Subjective Type Questions)
                 A.   Short answer type questions.
                     1.   Why is robustness important in AI ethics?

                     2.   What measures can be taken to ensure fair treatment by AI systems?
                     3.   What are the main goal of AI ethics as a multidisciplinary field?
                     4.   Provide an example of AI bias in the healthcare sector.

                     5.   Why is bias awareness important in AI technologies?
                 B.   Long answer type questions.
                     1.   Explain cognitive bias and how it can influence decision-making.
                     2.   How do games like the Moral Machine and Survival of the Best Fit help individuals understand ethical issues and
                          biases in AI?

                     3.   Why is transparency important in AI, and how can it be achieved in practical applications?
                     4.   What is the purpose of intelligence ethics in the development and use of AI technology?
                     5.   What measures can be taken to mitigate the effects of bias in AI systems?

                 C.   Competency-based/Application-based questions:                       #Experiential Learning

                     1.   Rahul works for a company that uses autonomous drones to deliver packages in urban areas. One day, a drone
                          encounters a malfunction while navigating through a busy city street. The drone’s AI must choose between making
                          an emergency landing in a park, where children are playing, or on a deserted road, risking damage to the drone and
                          its package.

                          The drone, programmed to minimise human harm, attempts to land on the deserted road but crashes into a parked
                          car, causing significant damage. The car owner, upset by the incident, demands accountability from the drone
                          company.
                          In  the  case  of  the  drone’s  malfunction,  who  should  be  held  accountable  for  the  damage,  and  what  ethical
                          considerations should guide the development of autonomous systems to handle such scenarios?
                          Assertion and Reasoning Questions:
                            Direction: Questions 2-3, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions
                          by selecting the appropriate option given below:
                          a. Both A and R are true and R is the correct explanation of A.
                          b. Both A and R are true but R is not the correct explanation of A.
                          c. A is true but R is false.
                          d. A is false but R is true.
                     2.   Assertion(A): Bias in AI systems can lead to unfair outcomes and perpetuate social inequalities.
                          Reasoning(R):  AI systems make decisions based on the data they are trained on, and if this data reflects societal
                                       biases or prejudices, the AI may replicate and amplify these biases in its decision-making process.
                                       This can result in unfair treatment of certain groups, exacerbating existing inequalities in areas such
                                       as healthcare, finance, and criminal justice.

                     3.   Assertion (A): Inputting skewed data into the system leads to AI bias.
                          Reasoning (R): AI systems never are as good as the data we feed them.









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