Class 10 Artificial Intelligence

Revisiting AI Project Cycle & Ethical Frameworks for AI

45 Class 10 Artificial Intelligence questions on Revisiting AI Project Cycle & Ethical Frameworks for AI from the CBSE board papers 2023–2026, each with its answer and a step-by-step solution.

Questions · page 3 of 3

1. Q41 CBSE 2023
   Under _________, one looks ​at various parameters ​which affect the ​problem we wish to ​solve, as this ​would ​make many ​lives better.
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   Problem Scoping. It is the first stage of the AI project cycle, in which one understands the problem, looks at the various parameters that affect it and sets the goal - before any data is collected.

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2. Q42 CBSE 2023
   Smart Assistants such ​as ​Alexa, Siri ​are ​the ​examples of :

   1. (a)Natural Language Processing
   2. (b)Data Science
   3. (c)Machine Learning
   4. (d)Computer Vision
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   (a) Natural Language Processing

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3. Q43 CBSE 2023
   4Ws Problem ​Canvas is ​a ​part ​of ​:

   1. (a)Problem Scoping
   2. (b)Data Acquisition
   3. (c)Modelling
   4. (d)Evaluation
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   (a) Problem Scoping

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4. Q44 CBSE 2023
   Explain ​any ​one ​example of AI ​bias.
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   A face recognition system trained mostly on light-skinned faces misidentifies darker-skinned faces far more often, because its training data did not represent everyone.

   A face recognition system trained mostly on light-skinned faces misidentifies darker-skinned faces far more often, because the data it learnt from did not represent everyone. The bias came from the training data, not from the machine.

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5. Q45 CBSE 2023
   What is the significance ​of ​AI project ​cycle ? Also explain ​in detail about how Data ​Acquisition is ​different ​from data ​exploration.
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   The AI project cycle orders the stages so none is skipped; acquisition collects raw data and exploration cleans and visualises it.

   The AI project cycle orders a project - Problem Scoping, Data Acquisition, Data Exploration, Modelling, Evaluation - so that nothing is skipped: the problem is understood before data is gathered, the data before a model is built, and the model tested before it is used. Data Acquisition gathers data from surveys, sensors and records, producing raw data. Data Exploration arranges, cleans and visualises that data so patterns become visible and the right model can be chosen. Acquisition collects; exploration understands.

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