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Ensure diverse data
Ensure the data is diverse. Speakers in the audio or video files should possess a range of characteristics, including locations, dialects, genders, sex, race, and nationality.
Sourcing such data could be difficult if you only rely on open-source data.
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AI models trained with biased data
Data bias can have notable implications for research and practical applications. For example, in a Facebook scandal, its AI shockingly asked users if they wanted to continue seeing videos about primates after watching a video featuring Black men.
Data bias refers to data sets that ...
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Monitor real-world performance
Once you've ensured your initial data set is diverse, your model can still have bias.
That's why you should monitor your model's real-world performance. For example, does your model better predict female speech over male speech? If so, retrain with new datasets to overcome any problem area...
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Collect data from a variety of sources.
The most common avenues for collecting training data:
- Paying for data sets
- Using public data sets
- Sourcing open source content
- Using in-person or field-collected data sets
A combination of all four will provide the best training data.
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