## 3 Data Bias
### Reporting Bias  
An additional type of bias that can be encountered in the data is the reporting bias. Reporting bias is quite common in Language Models (LMs) [57] and a plethora of studies have concluded that LMs such as RoBERTa and GTP-2 amplify reporting bias, due to the data that they have been trained on [58]. To address this issue, the authors in [59] propose a neural logic-based Soft Logic Enhanced Event Temporal Reasoning (SLEER) model in order to mitigate the reporting bias originating from the Temporal Common Sense (TCS) knowledge that is extracted from free-form text. In another paper, the authors focus on text-image datasets and the reporting bias that might occur in those cases [60]. They propose the bimodal augmentation (BiAug) approach that generates pairs of object-attributes to diminish the over-representation of recurrent patterns. However, the generation of data (i.e., pairs of object-attributes) is quite expensive in terms of computational resources due to the utilization of LMs. The authors suggest that this challenge can be mitigated by utilizing techniques such as parallel processing. In a similar approach focusing on image data [61], the authors suggest that the reporting bias can be removed by using label frequencies. Those frequencies, which are the per-class fraction of labeled and positive examples found in all positive examples, are being \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**ID** & **Year** & **Ref.** & **Domain** & **Data Category** & **Type** \\ \hline SB1 & 2019 & [39] & Environment & Time series & Identification \\ \hline SB2 & 2023 & [40] & Environment & Observations (numeric) & Identification \\ \hline SB3 & 2021 & [41] & Environment & Time series & Identification and Mitigation \\ \hline SB4 & 2018 & [42] & Computer Vision & Images & Identification and Mitigation \\ \hline SB5 & 2022 & [43] & Computer Vision & Images & Identification and Mitigation \\ \hline SB6 & 2023 & [44] & Computer Vision & Images & Identification and Mitigation \\  & & & (Health) & Images & Identification and Mitigation \\ \hline SB7 & 2019 & [45] & Health & EHR data (numeric and text) & Identification and Mitigation \\ \hline SB8 & 2023 & [46] & Health & Mixed (text and numeric) & Identification and Mitigation \\ \hline SB9 & 2023 & [47] & Health & Numeric & Identification and Mitigation \\ \hline SB10 & 2023 & [48] & Health & Text (NLP—Q&A) & Identification and Mitigation \\ \hline SB11 & 2021 & [49] & Health & Mixed (text and numeric) & Identification and Mitigation \\ \hline SB12 & 2017 & [50] & Not specific & Not specific & Identification and Mitigation \\ \hline SB13 & 2023 & [51] & Mixed (text and numeric) & Several (binary classification) & Identification \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Approaches to identify and/or mitigate selection bias. estimated using Dynamic Label Frequency Estimation (DLFE); thus, an increased accuracy of the debiasing process is observed. Similarly, in [62], it is suggested that a new variable should be added in the training dataset describing what is shown in every image, except for the image's label. In that way, this technique leverages both the aspects of human performance and the algorithmic understanding, thus showcasing quite promising results. In another approach [63], where the authors focus on the task of facial recognition, the authors experiment with five algorithms namely, logistic regression (i.e., LR), linear discriminant analysis (i.e., LDA), k-nearest neighbors (i.e., KNN), support vector machines (i.e., SVM) and decision trees (i.e., DT). Based on their results, DT seems to better address reporting bias that is present in the corresponding data. Regarding cybersecurity, two approaches can be found in the literature that focus on threat classification and malware detection, respectively.