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Published in:
2020 | OriginalPaper | Chapter
Syntactic and Semantic Bias Detection and Countermeasures
Authors : Roman Englert, Jörg Muschiol
Published in: Computational Science – ICCS 2020
Publisher: Springer International Publishing
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Abstract
Applied Artificial Intelligence (AAI) and, especially Machine Learning (ML), both had recently a breakthrough with high-performant hardware for Deep Learning [1]. Additionally, big companies like Huawei and Google are adapting their product philosophy to AAI and ML [2‐4]. Using ML-based systems require always a training data set to achieve a usable, i.e. trained, AAI system. The quality of the training data set determines the quality of the predictions. One important quality factor is that the training data are unbiased. Bias may lead in the worst case to incorrect and unusable predictions. This paper investigates the most important types of bias, namely syntactic and semantic bias. Countermeasures and methods to detect these biases are provided to diminish the deficiencies.