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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 don't represent the population in study. Models trained on biased data could contain prejudice. That's why AI Researchers and Data Scientists must be vigilant to ensure models don't contain any bias.
The most common avenues for collecting training data:
A combination of all four will provide the best training 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.
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 areas.
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