## 2 Materials and Methods  
As mentioned in the Introduction part, this manuscript aims to perform a comprehensive literature review regarding bias in ML, investigating the corresponding methods and approaches that have been proposed in order to identify and/or mitigate bias in the whole ML lifecycle. More specifically, in the context of this manuscript, the ML lifecycle consists of three (3) discrete stages for each there exist specific types of bias: (i) bias that can \begin{table}
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**Section** & **Description** \\ \hline Section 1 & Introduction to the scope of this manuscript and the overall work conducted \\ Section 2 & Methodology followed to carry out the comprehensive literature review \\ Section 3 & Analysis of approaches for identifying and mitigating data bias \\ Section 3.1 & Analysis of approaches for identifying cognitive bias in data \\ Section 3.2 & Analysis of approaches for identifying selection bias in data \\ Section 3.3 & Analysis of approaches for identifying reporting bias in data \\ Section 3.4 & Summarization of the most common approaches for mitigating data bias based \\ Section 4 & on the findings of Sections 3.1-3.3 \\ Section 4.1 & Analysis of approaches for identifying and mitigating algorithm bias \\ Section 4.2 & Research regarding the different estimators that have been utilized in the \\ Section 4.3 & literature, in diverse domains, and the way that they may introduce bias \\ Section 4.4 & Research regarding the different regularization methods that have been utilized in the \\ Section 4.3 & in the literature, in diverse domains, and the way that they may introduce bias \\ Section 4.4 & Summarization of the most common approaches for mitigating algorithm bias \\ Section 4.4 & based on the findings of Sections 4.1-4.3 \\ Section 5 & Analysis of approaches for identifying and mitigating engineer bias \\ Section 6 & Discussion of the findings of this manuscript and comparison to other literature \\ Section 7 & Summarization of the findings of this manuscript and provision of future \\  & research directions with regard to bias in ML \\ \hline \hline \end{tabular}
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Table 1: Table of the contents of this manuscript. originate from the data themselves (i.e., data bias), (ii) bias deriving from the ML models that are utilized (i.e., algorithm bias) or, (iii) bias by the ML engineers that develop and/or evaluate the produced ML models (i.e., engineer bias). Having divided the ML lifecycle into those categories, the types of bias that can occur in each category were identified, which on the one hand allowed the definition of the said biases, and on the other hand aided the identification of the corresponding approaches in the literature that manage to tackle those biases. An overview of the types of bias that can occur in each of the three (3) categories can be shown in Figure 1.  
When the different types of bias were identified, four (4) of the most widely known publications databases were selected, namely ACM Digital Library, IEEE Xplore, Scopus and Science Direct. For each database, the corresponding search query was generated. The search for relevant studies was based on the presence of specific search terms in the title, abstract or keywords of the said studies. For the data bias, the search terms consisted of the name of the corresponding bias (e.g., reporting bias), the phrase \"machine learning\", since this manuscript is focused on ML approaches that aim to address the issue of bias presence in data, and words like \"addressing\", \"mitigating\", \"identifying\" to further limit the search to studies that actually propose a way to identify and/or mitigate data bias. Regarding algorithm bias, more specifically estimator bias, another set of search terms was used. In the estimator bias, only studies that compare different algorithms and also take into consideration the aspect of bias were needed. As a result, words like \"bias\", \"comparative study\", and \"review\" were used as search terms.