## 1 Introduction  
Bias is the tendency to promote prejudiced results due to erroneous assumptions. In the context of ML and AI in general, this could be caused due to erroneous data on which an ML model has been trained, or due to other factors that will be further analyzed later in this manuscript. Algorithmic or not [1], bias is not a recent discovery and has been recognized by the research community for many decades. Looking back in history, it is quite clear that bias not only exists but also has played--and still plays--a crucial negative role in the evolution of humanity since the very early stages of its existence. Regardless of the era that one lives in, the concept of bias remains the lack of internal validity or even false assessment between an exposure and an effect that is expected to have a certain outcome that does not equal the real value [2]. Bias from the \"outside\" world can infiltrate AI systems through, for instance, the data that have been used for training the underlying ML models, thus affecting the results of the said systems. In essence, bias finds its way into AI systems and, given the fact that the adaptation of those systems is increasing exponentially [3], it is of vital importance to identify it and try to mitigate it, by utilizing a plethora of tools, algorithms and approaches that people have in their disposal. It is no surprise that in a very recent survey [4], bias is, alongside data privacy, one of the key aspects about which people are concerned when using AI tools. There have been several examples of AI tools, which have even been exploited in a production environment, that have produced erroneous results and predictions due to bias in the input data and/or the training process of the corresponding ML models. In deeper detail, an indicative example of such tools is Large Language Models (LLMs) like ChatGPT, which are known for inheriting bias from the training data, since the data themselves are not bias-free ([PERSON] et al., 2018; [PERSON] et al., 2018). This is a well-known issue of LLMs for which extensive research is being carried out to minimize such bias. Bias has occurred repeatedly in computer vision (CV) algorithms and applications as well. For example, in 2020 it was identified that the algorithm used by Twitter (now X) for cropping images favored light-skinned over dark-skinned individuals, whilst it also favored cropping female bodies instead of their faces, thus showcasing signs of both racial and gender bias ([PERSON] et al., 2018). This is not the only example of such an algorithm since both algorithms developed by Google and Apple for editing images have also showcased similar behaviors ([PERSON] et al., 2018). In those cases, the training data were also the culprit for the biased results generated by the algorithms. In another case, which sparked a nation-wide debate in the United States, a tool called COMPAS that was used as a Recidivism Prediction Instrument (RPI), was found to be biased in terms of race. The goal of this tool was to predict the probability of a criminal defendant reoffending at some time into the future ([PERSON] et al., 2018). It was discovered that this tool was biased against black defendants, and this was not only due to the training data but also due to the way that the underlying algorithm operates ([PERSON] et al., 2018). In the case of COMPAS, the nature of the algorithm relied on population effects, which were also wrong because the training data were biased. The law cares about individual cases and not population effects ([PERSON] et al., 2018); thus, the nature of the ML algorithm utilized in COMPAS was also responsible for the biased results that it produced. The above highlights the vital importance of mitigating any form of bias that might occur in AI tools in different domains since the presence of bias will inevitably lead to wrongful decision making.