@inproceedings{4e91972392424fb1a705fd819373a722,
title = "Syntactic and semantic bias detection and countermeasures",
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.",
keywords = "Bias detection, Multivariate regression, Root-out-bias, Training samples",
author = "Roman Englert and J{\"o}rg Muschiol",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 20th International Conference on Computational Science, ICCS 2020 ; Conference date: 03-06-2020 Through 05-06-2020",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-50423-6\_47",
language = "English",
isbn = "9783030504229",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "629--638",
editor = "Krzhizhanovskaya, \{Valeria V.\} and G{\'a}bor Z{\'a}vodszky and Lees, \{Michael H.\} and Sloot, \{Peter M.A.\} and Sloot, \{Peter M.A.\} and Sloot, \{Peter M.A.\} and Dongarra, \{Jack J.\} and S{\'e}rgio Brissos and Jo{\~a}o Teixeira",
booktitle = "Computational Science – ICCS 2020 - 20th International Conference, Proceedings",
address = "Germany",
}