What is one challenge in ensuring fairness in generative Al?
Fdaytalk Homework Help: Questions and Answers: What is one challenge in ensuring fairness in generative Al?
a) Unbiased model training
b) Using diverse and representative training data
c) Ignoring model biases
d) Focusing only on accuracy metrics
Answer
This question is about one of the main challenge(s) in ensuring fairness in generative AI. To solve this question, let’s analyze each option given and its relation to ensuring fairness in generative AI.
Given Options: Step by step analysis
a) Unbiased model training
- Unbiased model training is crucial and its actually a solution to ensuring fairness in generative AI, not a challenge.
b) Using diverse and representative training data
- This is also a solution to ensure fairness, as diverse data helps in reducing bias.
c) Ignoring model biases:
- This could be a challenge because if biases are ignored, the model may produce unfair or biased outputs.
d) Focusing only on accuracy metrics:
- Focusing solely on accuracy metrics without considering fairness can lead to biased models because accuracy does not account for fairness or bias.
Final Answer
The challenge in ensuring fairness in generative AI would be something that contributes to unfairness or bias in the model. Options c and d seem to fit this description.
- c) Ignoring model biases is directly about the challenge of not addressing biases, which leads to unfair outcomes.
- d) Focusing only on accuracy metrics also contributes to the challenge because it overlooks fairness.
Between the two, ‘ignoring model biases’ directly points out a challenge that leads to unfairness, while focusing on accuracy metrics is more about a misdirected focus rather than directly ignoring the fairness aspect.
Based on above analysis, the best answer is c) Ignoring model biases
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