## 3 Data Bias
### Selection Bias  
Another type of bias that can be identified in the data is the selection bias. Selection bias occurs when the data samples that are selected are not reflective of the real-world distribution of the data [34]. Under the umbrella of this kind of bias, there exists a plethora of subtypes of bias such as sampling, attrition, self-selection, survivorship, nonresponse and under-coverage bias [35]. In general, selection bias can be avoided by applying the proper techniques when sampling the population of interest and designing the corresponding study, thus understanding the connection between the data samples and the target population [36]. Except for the initial stages of a study, selection bias can also be addressed later on, when the data has already been collected. To achieve this, there have been proposed several methods in the literature, mainly statistical ones. Those statistical methods include the delta-method, linear scaling, power transformation of precipitation [37], empirical quantile mapping, gamma quantile mapping and gamma-pareto quantile mapping [38]. Selection bias is quite common in historical climate-related data; thus, the aforementioned methods are greatly used in relevant use cases, such as rainfall-runoff processes [39]. Another approach that aims to identify selection bias in environmental observational data is presented in [40], where the authors introduce two methods. The first one relies on the uniqueness of members of exponential families \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**ID** & **Year** & **Ref.** & **Domain** & **Data Category** & **Type** \\ \hline CB1 & 2023 & [25] & Natural Language Processing & Text & Identification \\  & & & (i.e., NLP) (Generic) & & \\ \hline CB2 & 2023 & [27] & NLP-Generic & Text & Identification \\ \hline CB3 & 2020 & [28] & Generic & Not specific & Identification \\ \hline CB4 & 2024 & [29] & NLP (Business) & Text & Identification \\  & & & & and Mitigation \\ \hline CB5 & 2020 & [30] & NLP (Business) & Text & Identification \\  & & & & and Mitigation \\ \hline CB6 & 2024 & [31] & Generic & Not specific & Identification \\  & & & & and Mitigation \\ \hline CB7 & 2022 & [32] & Computer Vision & Images & Identification \\  & & & & and Mitigation \\ \hline CB8 & 2024 & [33] & NLP (Health) & Text & Identification \\  & & & & and Mitigation \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Approaches to identify and/or mitigate cognitive bias. over any set of non-zero probabilities. The second method depends on the invariances of the true generating distribution before selection. The authors claim that both approaches generate adequate results for identifying the variables affected by selection bias. Similarly, in [41] an approach for mitigating selection bias in environment time series data is also presented. It consists of two cross-validation iterations. The outer one is used for identifying the set of variables based on which the corresponding predictor produces the minimum residual sum of squares (i.e., RSS), whilst in the inner iteration a grid search is performed to identify the optimal hyperparameters corresponding to the minimum RSS. Regarding computer vision, three approaches can be found in the literature. The first one is presented in [42] and makes use of a causally regularized logistic regression (i.e., CRLR) model for classifying data that contain agnostic selection bias. The model consists of a weighted logistic loss term and a subtly designed causal regularizer. The second one is showcased in [43], where the authors utilize an adapted triplet loss (i.e., ATL) method. In ATL, a matching loss term reduces the distribution shift between all possible triplets and the selected triplets, thus minimizing selection bias. The third one is presented in [44] and mainly focuses on medical images and utilizes a technique called Recalibrated Feature Compensation (i.e., RFC). RFC uses a recalibrated distribution to augment features for minority groups, by restricting the feature distribution gap between different sample views.