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4 cards from this section. Quick, good the night before a test.
What is meant by 'anonymization' in data collection?
Think about how to protect user identities.
Why is fairness important in model training?
Consider the impacts of biased data on decision making.
What is the key ethical focus during data collection?
Consider why privacy is important.
True or False: Bias is acceptable in AI if it results in better performance.
Think about fairness in decision-making.
Design a process to ethically collect data from a group that contains sensitive demographic information. Discuss how you would ensure consent and fairness.
Think about ethics and inclusion.
Propose a framework for monitoring AI systems to prevent bias. What metrics would you include and how would you analyze them?
Consider ongoing evaluations and user input.
6 more questions in this section. Enroll free to practise them all.
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