## 3 Data Bias
### Reporting Bias
The first approach makes use of four techniques for mitigating reporting bias in the data, namely covariate shift, kernel mean matching, Kullback-Liebler importance estimation procedure (i.e., KLIEP) and relative density ratio estimation [64]. In the second approach, the authors split the private and public datasets that they have at their disposal in different ways prior to training and testing the implemented convolutional neural network (i.e., CNN). The training and testing of the model occur in a disjoint way, using different timescales, thus mitigating the reporting bias of the training data [65]. In [66], the authors utilize a statistical technique called repeated measures correlation (i.e., RMCorr) to avoid reporting bias from health-related data, by using multiple samples from each subject. Similarly, the authors in [67] mainly focus on the data collection phase in order to avoid reporting bias by explicitly adjusting the survey responses related to the intention of patients to vaccinate. Those responses are then used to train a random forest (i.e., RF) algorithm that predicts whether a specific patient will be vaccinated on time. The approach presented in [68], also focuses on the data collection phase to mitigate potential reporting bias. In this case, the authors utilize specific software ([[https://acasillc.com/acasi.htm](https://acasillc.com/acasi.htm)]([https://acasillc.com/acasi.htm](https://acasillc.com/acasi.htm)), 22 March 2024) called audio computer-assisted self-interviewing (i.e., ACASI) and computer-assisted personal interviewing (i.e., CAPI) to provide a secure, private and confidential environment, thus increasing the level of answers' honesty to questions related to sensitive behaviors such as illicit drug use. Focusing on an entirely different domain and trying to estimate the dropout rate in college institutions, the authors in [69] propose Disc-Less (Discrimination-Less) which is based on the advanced process optimizer (i.e., ADOPT). In a similar case [70], where the authors try to predict whether a university student will fail a class, they compare three algorithms which are LR, Rawlsian and cooperative contextual bandits (i.e., CBB). LR has the highest accuracy but is prone to bias. Rawlsian is a fairness algorithm but in this specific case fails to remove bias. Finally, CBB consists of two Gradient Contextual Bandits, which not only mitigate reporting bias that is present in the data but are also easier to implement in contrast to other similar baseline models such as Learning Fair Representation (i.e., LFR) and Adversarial Learned Fair Representation (i.e., ALFR). In [71], a complete toolkit is presented that allows the users to compare thirteen ML algorithms and utilize twelve (12) evaluation metrics, as well as perform a significance test to reduce reporting bias. The results of this toolkit are very promising; however, it has been utilized only for aerospace prognostic data. In [72], Bosco is introduced to deal with imbalanced biology data. The main rule that Bosco follows is assigning samples in the major group with different important scores, thus mitigating potential bias in the data. As for time series data, the authors in [73] compared two known techniques, namely quantile mapping and power transformation to correct the data coming from several different regional climate models. Their results showcased that power transformation outperformed quantile mapping, since the corrected data that power transformation generated, enhanced the accuracy of the model that was trained based on them. In [74], the authors suggest that Privacy-Preserving Decentralized Intelligence can also be utilized to avoid bias by training ML models over a network of autonomous entities without the training data being exposed. Except for the model training, data analysis can also take place in a distributed manner, which could also help mitigate bias.