Bias in Machine Learning

Bias in Machine Learning

12th Grade

10 Qs

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Bias in Machine Learning

Bias in Machine Learning

Assessment

Quiz

Computers

12th Grade

Practice Problem

Easy

Created by

Mr McCallion

Used 1+ times

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10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What are the different types of bias that can occur in machine learning?

selection bias, confirmation bias, algorithmic bias

label bias

interpretation bias

data bias

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How can data preprocessing help mitigate bias in machine learning models?

By identifying and removing biased features, balancing the dataset, and applying techniques like data augmentation.

By using biased algorithms exclusively

By introducing more bias into the dataset

By ignoring the bias and proceeding with the raw data

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Explain the concept of algorithmic bias and its impact on decision-making processes.

Algorithmic bias only affects certain individuals or groups

Algorithmic bias is the unfair discrimination in decision-making processes caused by biased algorithms, leading to perpetuating inequalities and harm to individuals or groups.

Algorithmic bias has no impact on decision-making processes

Algorithmic bias is a positive influence on decision-making processes

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What role does interpretability of machine learning models play in addressing bias?

Interpretability of machine learning models has no impact on bias detection

Interpretability of machine learning models plays a crucial role in identifying and addressing bias by allowing for the detection and mitigation of biased decisions.

Interpretability of machine learning models leads to increased bias in decision-making

Interpretability of machine learning models is only useful for improving accuracy, not bias detection

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Discuss the importance of diversity in training datasets to reduce bias in machine learning.

Bias in machine learning can be eliminated without diverse training datasets.

Training datasets with bias are more effective in machine learning.

Diversity in training datasets is not necessary for reducing bias in machine learning.

Diversity in training datasets is crucial to reduce bias in machine learning.

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How can fairness metrics be used to evaluate and mitigate bias in machine learning algorithms?

Fairness metrics cannot identify biases in machine learning algorithms

Fairness metrics are only used for performance evaluation, not bias mitigation

Fairness metrics can be used to identify biases in machine learning algorithms and then implement mitigation strategies to address them.

Fairness metrics are irrelevant in machine learning algorithm development

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Explain the concept of feedback loops in biased machine learning systems.

Feedback loops in biased machine learning systems have no impact on the system's predictions

Feedback loops in biased machine learning systems are beneficial for improving accuracy

Feedback loops in biased machine learning systems always lead to unbiased outcomes

Feedback loops in biased machine learning systems occur when the system's predictions or decisions are based on biased data, and the feedback received from these decisions further reinforces the biases in the data.

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