How can we ensure data diversity in AI training?
Ensuring data diversity involves using a wide range of data sources that represent different demographics and perspectives.
What are some strategies to collect diverse data?
You can use techniques like crowd-sourcing, partnerships with diverse organizations, and leveraging open data repositories.
Why is data diversity important for AI?
Data diversity is crucial to avoid bias, improve model accuracy, and ensure that AI systems serve all users fairly.
Can you give examples of biased AI outcomes?
Sure! Examples include facial recognition systems misidentifying people of color or hiring algorithms favoring certain demographics.
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