CAIS EXAM PREPARATION

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Question #11 (Topic: demo questions)

What is a common drawback of a highly transactional leadership style in AI engineering?

It allows for flexible roles and responsibilities within the team

It can lead to low motivation and creativity among team members

It fosters strong team collaboration and high engagement

It encourages team members to take initiative and innovate

Correct Answer: B

Explanation:

A highly transactional leadership style can lead to low motivation and creativity among team members.
This style focuses on rewards and penalties, which may not inspire or engage team members effectively,
especially in environments requiring creativity and innovation

Question #12 (Topic: demo questions)

What role does the concept of "fairness" play in the development of AI systems, and how can it be
quantified?

Fairness refers to the unbiased treatment of different groups or individuals by the AI system, which
can be quantified using metrics like disparate impact or equal opportunity difference.

Fairness ensures that all data points are treated equally during training, which can be quantified by
measuring model accuracy.

Fairness is about reducing model complexity to prevent overfitting, and it can be quantified by
analyzing model variance.

Fairness focuses on optimizing the model's performance across all test sets, which can be
quantified using cross-validation scores.

Correct Answer: A

Explanation:

Fairness in AI refers to the unbiased treatment of individuals or groups by the AI system, ensuring that
the model’s predictions or decisions do not favor one group over another based on attributes like race,
gender, or socioeconomic status. This concept is critical in avoiding discrimination and ensuring
equitable outcomes, particularly in applications like hiring, lending, and law enforcement. Fairness can
be quantified using various metrics, including disparate impact, which measures the difference in
outcomes between different groups, or equal opportunity difference, which assesses the consistency of
true positive rates across groups. Addressing fairness involves careful data selection, algorithm design,
and post-deployment monitoring to detect and mitigate biases that could lead to unfair treatment.

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