## 3 Data Bias
### Cognitive Bias  
Cognitive bias is mostly related to the nature of the data. For example, text data from social media posts are quite likely to represent the cognitive bias of the people who wrote those posts. This kind of bias is several times identified in LLMs [24], since they are usually trained, among others, on this kind of data. At this point, it is important to note that this kind of human bias is not related to the bias caused by the ML model builders and evaluators. They are both caused by humans, but the first one is interconnected with the data, whilst the second one is associated with the implementation and evaluation of the ML models. There have been several attempts to identify cognitive bias in text data such as the one presented in [25]. In this paper, the authors try to automatically detect 188 cognitive biases that are present in the 2016 Cognitive Bias Codex [26] and test its performance to both human and AI-generated text data, showcasing promising results. However, the authors also state that they need more human participants in their study in order to use their responses as the ground truth and establish a better understanding of the approach's accuracy. In a similar approach that also focuses on text data [27], where the authors try to mitigate cognitive bias that can be found in text data, an ontology-based approach named Automatic Bias Identification (i.e., ABI) is showcased. The main idea of ABI is to consider the decisions made by the users in the past in order to identify whether new, different decisions made by the same users contain cognitive bias. In essence, based on the historical decisions available to the tool, it performs a Boolean classification regarding whether a new decision is biased or not. In [28], the authors aim to identify four types of cognitive bias in ML models by allowing users to provide feedback to the said models and then examining whether this feedback increases or decreases the variance of future choices made by the ML models. In [29], BiasEye is introduced, which aims to identify and mitigate cognitive bias in text data and, more precisely, text data coming from curriculum vitae (i.e., CVs). BiasEye depends on an ML approach that models individual screening preferences so that it improves information transparency and accessibility, thus enhancing awareness regarding cognitive biases. Similarly, regarding text data from CVs, the authors in [30] propose a three-step approach for mitigating cognitive bias, consisting of pre-process, in-process and post-process steps. More specifically, for the pre-process step, they examine three techniques, namely optimized pre-processing (i.e., OPP), massaging-reweighting-sampling (i.e., MRS) and disparate impact reduction (i.e., DIR). Regarding the in-process step, they utilize adversarial debiasing (i.e., AD) and prejudice remover (i.e., PR). As for the post-process step, they test three techniques namely equalized odds post-processing (i.e., EOP), calibrated equalized odds post-processing (i.e., CEOP) and reject option classification (i.e., ROC). Based on their experiments, they conclude that the combination of DIR, AD and EOP achieves the best results but has higher complexity and computational cost, whereas without the utilization of AD, the accuracy is slightly lower but, still, quite promising. In [31], the integration of causal analysis results is suggested in order to mitigate cognitive biases. The authors argue that this integration, as well as the utilization of knowledge discovery mechanisms, alongside data-driven methodologies, will assist in avoiding cognitive biases. In another study [32], the authors also propose an approach to not only identify cognitive bias but also mitigate it, utilizing a module that alters data attributes that cause cognitive bias and then evaluates the results by measuring the KL divergence value. Their approach has been tested in computer vision--related datasets and more specifically in image data, showcasing promising results.