Practice Mitigation Strategies (15.7.4) - Natural Language Processing (NLP)

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9 questions

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6 sample questions

Question 1.
What is the purpose of using diverse datasets in NLP?
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Think about perspectives.
Question 2.
What does regular auditing of AI systems help identify?
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What can go wrong?
3Question 3.
What is a key strategy for reducing bias in NLP?
- Using a single data source
- Using diverse datasets
- Ignoring bias
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What type of dataset would be most inclusive?
4Question 4.
Regular audits in AI systems help to identify:
- User preferences
- Technical bugs
- Biases
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What could go wrong in AI applications?
Question 5.
Propose changes to an NLP application that faced backlash for biased outputs. Detail your approach.
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What would you change in the data and monitoring methods?
Question 6.
Evaluate the effectiveness of current bias mitigation strategies in an AI tool and suggest improvements.
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Where could the tool improve its processes?

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