Source: thenewstack.io
Uncovering Biases: The Importance of Data Diversity in Speech RecognitionCategory: Data, artificial-intelligence
Companies can use artificial intelligence (AI) and machine learning (ML) models for a variety of reasons such as reviewing job candidates, monitoring employee productivity, or analyzing voice data to better understand customer’s needs. ML models are typically trained to recognize certain types of patterns over a set of data, providing an algorithm they can use to reason over and learn from.
The first step to eliminating bias in model training is to acknowledge that inherent bias does exist in ML models.
The next and most important step to reducing bias in ML models is to have a diverse data set.
Yet, it’s critical that you acknowledge bias exists and how it came about, to understand the importance of employing a diverse data set.
The first step to eliminating bias in model training is to acknowledge that inherent bias does exist in ML models.
The next and most important step to reducing bias in ML models is to have a diverse data set.
Yet, it’s critical that you acknowledge bias exists and how it came about, to understand the importance of employing a diverse data set.
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