## 7 Conclusions  
To summarize, bias is a key factor in ML that will further trouble researchers during the exponential growth and adaptation of AI systems in everyday life. It is imperative for everyone who is involved in training, evaluation or even using AI systems to (i) identify the biases that may be present in the underlying ML model and (ii) apply the appropriate methods to mitigate them. As mentioned earlier, bias is almost impossible to eliminate, but a more realistic goal is to minimize it. As shown in this manuscript, bias has many forms that can be identified during the different stages of the ML lifecycle. In the context of this manuscript, the types of bias were grouped into three main categories, namely bias originating from the data, the ML models and the ML engineers, respectively. For each of those categories, the types of bias were presented, as well as the methods and approaches that have been proposed in the literature in order to mitigate them. Based on the findings of this study, it appears that the current literature mainly focuses on specific biases in ML, mostly related to the data, whilst it either underestimates or omits other types of biases, especially when it comes to evaluating the proposed ML models. Researchers undoubtedly try to mitigate bias in their approaches; however, they should start exploring other aspects of it and try to mitigate it throughout the whole ML lifecycle and not on isolated stages. To this end, this manuscript not only serves as a guideline for ML engineers regarding possible biases and ways to mitigate them but also provides a direction for future research, especially focusing on the existing terms and approaches of ML models' biases, where the ML engineers should better concentrate on. Of course, this manuscript could be further extended in terms of the existing AI fairness tools that, in the context of this study, were briefly described. Moreover, the context of this manuscript could be transformed into a technical implementation that would pave the way for a more complete mitigation of bias in ML, regardless of the provided use case scenario. The aforementioned technical implementation could then also be evaluated in the context of several related EU projects in which the authors of this manuscript have participated [257; 258; 259; 260; 261; 262] to extract more valuable results and potentially update the technical implementation accordingly to better tackle bias-related challenges in the corresponding use cases. Conceptualization, K.M. ; methodology, K.M., A.M. ([PERSON]) and A.K. ; validation, A.M. ([PERSON]), A.K. and K.M. ; formal analysis, K.M. ; investigation, K.M. and A.M. ([PERSON]); resources, K.M. and A.K. ; writing--original draft preparation, K.M. and A.M. ([PERSON]); writing--review and editing, K.M., [PERSON] ([PERSON]), A.K. and A.M. ([PERSON]); visualization, K.M. ; supervision, A.M. ([PERSON]) and D.K. ; project administration, D.K. ; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript. Funding: The research leading to the results presented in this paper has received funding from the European Union's funded Projects AI4 Gov under grant agreement no 101094905 and XR5.0 under grant agreement no 101135209. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflicts of interest. References References
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