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SSC CHSL 14 Nov 2025 Shift 3 Paper
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Question Numbers: 7-11
Read the following passage and answer the questions based on it:
The explosive integration of Artificial Intelligence (AI) into the workplace represents a tangled web of ethical issues that need to be carefully addressed. While Al holds out the potential for increased productivity, mechanisation of routine tasks, and new opportunities for economic development, it raises to its highest power fundamental questions regarding job loss, algorithmic bias, and the redefinition of human work. Strong retraining initiatives and proactive legislative responses are necessary to counteract social disruption in the face of the possibility of widespread job losses, particularly in repetitive and predictable tasks. More dangerously, Al programs that learn from historical data have the potential to reinforce and even magnify prevailing cultural biases, leading to biassed performance evaluations or discriminatory hiring practices. This creates issues of fairness and equity in a more automated world. In addition, as Al fills increasing numbers of decision-making positions, the accountability issue is at the forefront: who is accountable when an Al system commits a critical mistake or inflicts harm? Strong retraining initiatives and proactive legislative responses are necessary to counteract social disruption in the face of the possibility of widespread job losses, particularly in repetitive and predictable tasks. More dangerously, Al programs that learn from historical data have the potential to reinforce and even magnify prevailing cultural biases, leading to biassed performance evaluations or discriminatory hiring practices.
Read the following passage and answer the questions based on it:
The explosive integration of Artificial Intelligence (AI) into the workplace represents a tangled web of ethical issues that need to be carefully addressed. While Al holds out the potential for increased productivity, mechanisation of routine tasks, and new opportunities for economic development, it raises to its highest power fundamental questions regarding job loss, algorithmic bias, and the redefinition of human work. Strong retraining initiatives and proactive legislative responses are necessary to counteract social disruption in the face of the possibility of widespread job losses, particularly in repetitive and predictable tasks. More dangerously, Al programs that learn from historical data have the potential to reinforce and even magnify prevailing cultural biases, leading to biassed performance evaluations or discriminatory hiring practices. This creates issues of fairness and equity in a more automated world. In addition, as Al fills increasing numbers of decision-making positions, the accountability issue is at the forefront: who is accountable when an Al system commits a critical mistake or inflicts harm? Strong retraining initiatives and proactive legislative responses are necessary to counteract social disruption in the face of the possibility of widespread job losses, particularly in repetitive and predictable tasks. More dangerously, Al programs that learn from historical data have the potential to reinforce and even magnify prevailing cultural biases, leading to biassed performance evaluations or discriminatory hiring practices.
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