Ethical AI Exercises (Part 3)

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Question 1 of 6

1. Question
Why is removing human bias from ML training data important?
- Removing human bias from data used to train machine learning models will improve their speed of performance and reduce their operating costs.
- Machine learning systems are highly dependent on accurate data to provide accurate results. Biased data will result in biased outputs
- Removing human bias from data used to train machine learning models is important to simplify vendor selection.
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Question 2 of 6

2. Question
What are some of the main elements of Societal Ethics. Select all that apply.
- Human Values
- Safety & Security
- Dignity
- Fairness
- Human Accountability
- Respect of the Environment
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Question 3 of 6

3. Question
What does “dignity” mean in the context of Ethical & Responsible AI? (select the best answer)
- AI systems should consider humans to be a component of the overall system
- Humans should not feel that AI is the secret to all their problems
- Organizational owners shouldn’t overinvest in AI systems
- AI systems should not be built to treat humans as machines
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Question 4 of 6

4. Question
What sort of potential accessibility challenges do you need to address as part of an Trustworthy AI Framework? (select all that apply):
- Inability for users to understand AI results
- Inability for users to access physical interaction with the AI system
- Inability for users to develop AI systems
- Inability for data scientists to properly model AI systems
- Inability for users to understand or visualize AI system results
- Inability for users to interact with conversational systems
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Question 5 of 6

5. Question
What aspects of “Do no harm” are relevant for Ethical AI? (Select all that apply)
- Physical Harm
- Freedom Harm
- Economic Harm
- Emotional & Mental Harm
- Environmental Harm
- Vendor Harm
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Question 6 of 6

6. Question
When creating the Societal Ethical AI Framework, Human Benefit should be placed above all other considerations.
- True
- False
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Ethical AI Exercises (Part 3)

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