## 2 Fundamental Concepts and Background
### Explainable AI (XAI)
#### 2.2.1 The Need for XAI: Fair and Ethical Decision-Making  
\"Black box\" AI systems that give prediction without any explanation are problematic for numerous reasons, not only because of their lack of transparency but also because they hide potential biases within the system [50]. There are many examples where bias in AI-based predictive systems has been uncovered. These systems have been shown to reinforce social and historical human prejudices and people who are traditionally marginalized in our society are disproportional negatively impacted [8]. Predictive software used in courtrooms to assess the likelihood of recidivism have proven to be extremely unreliable due to their bias towards race, revealing higher scores for Black people [51]. For example, the AI-based system COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) has been widely criticized for being unreliable and racially biased [52]. Several case studies have demonstrated that \"dirty data\"used in policy-making systems have led to skewed predictions [53]. Another example of bias in AI concerns search engines that tend to favor certain sites over others revealing a political bias [54], and hiring algorithms tainted by \"societal noise\" tend to perpetuate discriminatory behaviors impacting certain individuals or groups [55]. This algorithmic discrimination is also observed in systems such as targeted advertisements where gender discrimination occurred in the display of STEM career ads [56; 57]. Moreover, some vision detection systems have demonstrated a bias toward subject skin tone. This predictive inequity has been characterized by higher performance for lower Fitzpatrick skin tones [58]. These cases, to mention but a few, illustrate how such systems deployed in a real-world context can become \"Weapons of Math Destruction\" reinforcing inequalities [59]. Similarly issues arise when tools built on biased data are used in precision medicines [60] and there are many examples of medical datasets where the lack of inclusion of minorities has led to the development of biased models. For example, European populations were found to be significantly over-represented, while the other races were underrepresented in genomic studies in US [61]. Another example concerns the Framingham Heart Risk functions used to assess the risk of coronary heart diseases which suffered from an overestimation of risk for the German population [62]. This bias was due to the Caucasian sample the initial study was based on. This reveals the particular attention needed in order to implement AI-based models developed on medical datasets. [PERSON] et al. [63] determined that existing XAI techniques cannot provide explanations that adequately identify discriminatory behavior in some sensitive applications. Although, giving explanations can increase understanding of and trust in a system [19; 46; 64], simple explanations can hide undesirable attributes of the system and may mislead users into coming to dangerous or unfounded conclusions that could ultimately be unethical [21]. Additionally, an awareness of the dangers of blindly embracing explanations that may disguise racial or gender discrimination [46] or provide fair-washing, i.e., \"promoting the false perception that a ML model respects some ethical values\" [65] is needed in all medical areas where such systems may be utilized. In this paper, we focus on XAI as a technician solution that can help to expose systemic bias in CDSS; however, this does not address underlying deep-rooted discriminatory assumptions [8]. XAI can be used to help us evaluate if predictions are biased and defend algorithmic decisions as being fair and ethical [18; 19; 46; 66]. Additionally, XAI can help to shed some light onto causality [18; 46; 67] although, there is a recognized need to go beyond causality/correlation to true \"causability\" [3].