Prepare for the Revisiting AI Project Cycle & Ethical Frameworks for AI Class 10 Extra Questions and Answers. This resource covers key concepts of the AI Project Cycle and ethical frameworks in Artificial Intelligence in a simple, exam-oriented format. Students can use these questions for revision and Class 10 board exam preparation.
Revisiting AI Project Cycle & Ethical Frameworks for AI Class 10 Extra Questions and Answers
Q. The deployment stage of the AI project cycle is crucial for:
a. Collecting initial data.
b. Training the model.
c. Ensuring successful integration in a real-world environment.
d. Exploring data patterns.
Answer: c. Ensuring successful integration in a real-world environment
Q. Which of the following is NOT a principle of bioethics?
a. Respect for Autonomy
b. Do not harm anyone.
c. Ensure maximum benefit for all
d. Maximise profit
Answer: d. Maximise profit
Q. Assertion (A) : Frameworks serve as a common language for communication and collaboration.
Reason (R) : Frameworks do not facilitate sharing of best practices.
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)
Answer: c. (A) is true, but (R) is false.
Q. Ethical frameworks are primarily designed to:
a. Increase the efficiency of AI algorithms.
b. Ensure that choices made do not cause unintended harm.
c. Reduce the cost of AI development.
d. Speed up the AI project cycle.
Answer: b. Ensure that choices made do not cause unintended harm.
Q. Which stage of the AI Project Cycle involves testing the model on newly fetched data ?
a. Data Exploration
b. Modelling
c. Evaluation
d. Deployment
Answer: c. Evaluation
Q. As AI is essentially being used as a decision-making / influencing tool, we need to ensure that AI makes morally acceptable recommendations. Which of the following is a key factor that can knowingly or unknowingly influence our decision-making while designing an AI model?
a. Intuition and Values
b. Algorithm efficiency
c. Data storage capacity
d. Processing speed
Answer: a. Intuition and Values
Q. Assertion (A) : Bioethics is an example of a value-based framework for AI.
Reason (R) : Bioethics deals with ethical issues related to health, medicine, and biological sciences.
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.
Answer: a. Both (A) and (R) are true and (R) is the correct explanation of (A).
Q. ___________ enables computers to mimic human intelligence.
a. Cloud Computing
b. Artificial Intelligence
c. Web Development
d. Data Entry
Answer: b. Artificial Intelligence
Q. Which of the following is the second stage of an AI project cycle?
a. Problem Scoping
b. Evaluation
c. Data Acquisition
d. Data Exploration
Answer: c. Data Acquisition
Q. Kinaesthetic Intelligence: (It is not given in the syllabus but is asked in the board exam.)
a. Is the ability that is related to how a person uses his limbs in a skilled manner.
b. Assesses one’s proficiency in mathematics and logical reasoning.
c. Describes the level of self-awareness someone has, starting from realising weaknesses and strengths to recognising their own feelings.
d. The ability to perceive the visual world and the relationship of one object to another.
Answer: a. Is the ability that is related to how a person uses his limbs in a skilled manner.
Q. Which AI application involves analysing large sets of data to extract meaningful insights for decision-making?
a. Computer Vision
b. Robotics
c. Natural Language Processing (NLP)
d. Data Science
Answer: d. Data Science
Q. __________ is the best example of robotics and AI (artificial intelligence) working together.
a. Telephones
b. Self-driving cars
c. Bulb
d. Printing Machine
Answer: b. Self-driving cars
Q. Which of the following statements is true about AI bias?
a. It is always negative.
b. It leads to overfitting of training data.
c. It can result in unfair and discriminatory outcomes.
d. A machine can have biases of its own.
Answer: c. It can result in unfair and discriminatory outcomes.
Q. Differentiate between deep learning, artificial intelligence and machine learning. Also draw a labelled Venn diagram depicting the relationship between AI, ML and DL.
Answer:
- Artificial Intelligence (AI) is the broad field of computer science where machines are made to act like humans by solving problems, learning, and making decisions.
- Machine Learning (ML) is a part of AI in which machines learn patterns from data and improve their performance without being directly programmed.
- Deep Learning (DL) is a part of ML that uses artificial neural networks with many layers to learn from very large amounts of data, such as recognising faces or voices.
In a Venn diagram, AI is shown as the largest circle, ML is inside AI, and DL is inside ML, showing that DL is a subset of ML and ML is a subset of AI.
Q. Consider the following scenarios and identify which AI domain would be most appropriate for each, with justification:
a. An AI-based education platform needs to translate to the English language and analyse thousands of student essays to provide instant feedback on grammar, content quality and writing style.
Answer: The education platform that translates and checks essays belongs to the domain of Natural Language Processing (NLP) because NLP helps computers understand human language, grammar, and writing style.
b. An AI-based application installed on a busy crossing in a metropolitan city scans all vehicles driving through that crossing during peak traffic hours and categorises them into four-wheelers and two-wheelers.
Answer: The traffic application that scans vehicles belongs to the domain of computer vision because it allows machines to see and analyse images or videos and then classify vehicles into two‑wheelers and four‑wheelers.
Q. What do you do in the second and third stages of an AI project cycle?
Answer:
- The second stage of the AI Project Cycle is Data Acquisition, where data is collected from reliable and authentic sources. This data becomes the foundation of the project.
- The third stage is data exploration, where the collected data is studied using graphs, charts, and maps to find patterns. These patterns help in deciding which model should be built to solve the problem.
Q. Explain the difference between sector-based and value-based ethical frameworks with one example of each.
Answer: Sector‑based ethical frameworks are made for specific industries. For example, bioethics in healthcare ensures patient privacy, safety, and fair use of medical data. Value‑based ethical frameworks are based on universal moral values. For example, a rights‑based framework ensures respect for human dignity, autonomy, and freedom. Thus, sector‑based frameworks focus on particular fields, while value‑based frameworks focus on general moral principles.
Q. Read the case study below and answer the following questions:
A school develops an AI system to shortlist students for a competitive scholarship. The algorithm is trained to prioritise students who complete online application forms quickly — assuming that faster completion reflects confidence, competence, and techsavviness. However, students who are not fluent in English, or who do not have access to a computer at home, take longer to fill out the form. As a result, many deserving students are unfairly rejected by the algorithm.
a) Identify two reasons why the algorithm gave biased results.
Answer:
- It assumed that filling the form quickly means confidence and competence.
- It ignored students who are weak in English or do not have computer access, so they took longer and were unfairly rejected.
b) Mention two bioethics principles that can help solve such a problem and explain how they apply.
Answer:
- Justice: Justice means treating all students fairly, without discrimination based on language or computer access.
- Do not harm: Do not harm means avoiding unfair rejection of deserving students. By applying these principles, the AI system will become more fair and ethical.
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