iSQI Exam CT-AI Topic 2 Question 13 Discussion

Actual exam question for iSQI's CT-AI exam

Question #: 13
Topic #: 2

An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?

SELECT ONE OPTION

AThis is an example of expert system bias.

BThis is an example of sample bias.

CThis is an example of hyperparameter bias.

DThis is an example of algorithmic bias.

Show Suggested Answer

Suggested Answer: A

When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:

Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.

Why Not Other Options:

Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.

Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.

GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.

by Rickie at Sep 14, 2024, 12:02 PM

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Gayla

3 days ago

I'm not sure, but I think it could also be D) This is an example of algorithmic bias. The system may not be trained properly to recognize faces from all ethnicities.

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Dona

7 days ago

I agree with Thaddeus. The data distribution is not representative of the actual population, so it's definitely sample bias.

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Gaston

8 days ago

This is definitely an example of sample bias. The training data is heavily skewed towards ethnicity A, which could lead to the model performing poorly on the less represented ethnicities.

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Thaddeus

12 days ago

I think the answer is B) This is an example of sample bias.

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