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Ensure diverse data

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

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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