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The rapid integration of artificial intelligence (AI) assistance in a plethora of business and lifestyle domains presents a pressing demand for increasing AI transparency and accountability to ensure user trust, regulatory support, and system design integrity. Explainable AI (XAI) is an emerging research domain that traverses the intersection of computer and social sciences, providing innovative methods focused on increasing transparency and interpretability of AI systems, particularly concerning biased algorithmic action and decision production that affects user well-being. Despite the technological advancements present in the unprecedented accuracy improvements of AI systems, limitations remain regarding the transparency of AI functionalities and decision processes, and the inherent biases riddling those aspects. There is significant public interest, concern, and belief regarding AI transparency; however, there exists a restrained cross-section of users who believe they would benefit from AI systems and action decisions being fully explainable and interpretable (Caruana et al., 2000;  Lundberg & Lee, 2017; Chen & Zhao, 2020).

Current systems deploy a modicum of transparent design solutions that assume transparency is a one-size-fits-all feature. Without exception, these methods are superficial and nascent. Interestingly enough, there is an observable information asymmetry that exists between governments, policy-makers, and businesses that seek to adopt permissive regulations around the use of AI. One of the common approaches being utilized to address the innate concerns of these parties is applying ethical principles or guidelines upon introducing XAI systems. These principles usually outline the desired or intended outcomes of these respective technologies and system actions, rather than the means to design those systems responsibly and ethically. We contend that the introduction of ethical principles and guidelines cannot depend upon ad hoc implementations. Principles cannot be defaulted on to be universal solutions, rather their intended use must be specific and context-appropriate within the systems and organizational missions for which they are designed. Therefore, we urge that organizations thoughtfully customize their principles based on a true understanding of their business domain, mission, and culture that conveys the logic and outcome of their principle selections (Ribeiro et al., 2016; Zhang & Li, 2021).