… bias in machine learning (ML) projects. When ML-capabilities are used for decision making,
they frequently affect the lives of many people. However, bias can … gender inequality [15]. …

… the information about the features causing bias, we propose a preprocessing approach …
machine learning by reducing the bias caused by several features such as age, race and gender…

… Gender bias in machine learning occurs when supervised models predict based on spurious
societal correlations in their training data. This may result in harmful behaviour when it …

… gender bias in machine translation through a case study of Google Translate. We think that
by investigating bias in machine learning … consequences of machine learning algorithms and …

… bias concerns unlawful bias, that is, the targeting of people based on prohibited grounds. This
may be a subset of ethical bias, but sometimes bias … the basis of gender may be prohibited …

… This section aims to measure gender bias on different multilingual architectures to understand
their difference when encoding source sentences and decoding target ones. We will focus …

… may also have been shaded by racial 5 and gender bias,'6 and ultimately reflected in her
marks or reference letters from educators.Similar gender biases may have influenced her male …

… While our work serves as an initial approach toward unsupervised detection of comment-level
gender bias, we identify several limitations and areas for future work. We first focus on …

… a gender biased corpus, structured models such as conditional random fields, amplify the
bias. … co-related in vSRL, we define the gender bias toward man for each verb b(verb,man) as: …

… of bias In automated business processes, machinelearning … Machine learning also
promises to improve decision quality, … biases of past human decision makers, such as gender …