When collecting and preparing data for AI models, it’s crucial to consider factors like data collection methods, sampling, labeling, representation, privacy preservation, and potential biases. Learn the key questions to ask to ensure your data is properly prepared for AI modeling.
Table of Contents
Question
Which of the following questions will you consider while collecting and preparing data for your model. Select all that apply.
A. How is my data collected? How is it sampled and labelled?
B. What does my data represent?
C. Have I sliced the data accurately?
D. How do I preserve my user’s privacy?
E. How is my model performing across my diverse user base?
F. Are there any underlying biases that I might reinforce?
Answer
A. How is my data collected? How is it sampled and labelled?
B. What does my data represent?
D. How do I preserve my user’s privacy?
F. Are there any underlying biases that I might reinforce?
Explanation
The correct answers are:
A. How is my data collected? How is it sampled and labelled?
This is important to consider because the way data is collected, sampled, and labeled can significantly impact the quality and representativeness of the dataset used to train the AI model. Biased or non-representative data collection and labeling can lead to skewed models.
B. What does my data represent?
Understanding what your data actually represents is critical. If the data does not accurately capture the real-world phenomenon or problem domain the AI model is intended to address, the model’s outputs will not be meaningful or useful.
D. How do I preserve my user’s privacy?
Privacy preservation is a key ethical and legal consideration when collecting data for AI. Proper anonymization techniques, data access controls, and compliance with relevant privacy regulations (like GDPR) are essential.
F. Are there any underlying biases that I might reinforce?
Examining the dataset for potential biases, both in the way the data was collected/sampled and in the data itself (like skews in demographic representation) is crucial. If underlying biases are not addressed, the AI model risks perpetuating or even amplifying those biases in its outputs.
Options C and E, while relevant considerations when evaluating model performance, are not directly related to the data collection and preparation stage that the question is focused on.
Google AI for Anyone certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Google AI for Anyone exam and earn Google AI for Anyone certification.