Leveraging Context-Sensitivity for User-Centered Explainability

The widespread integration of artificial intelligence (AI) into our daily lives has spurred an escalating demand for explainable AI (XAI). This demand is particularly pronounced in critical domains such as healthcare and finance, where understanding the decision-making processes of AI models is paramount. Despite noteworthy strides in XAI, prevailing approaches often neglect the crucial dimension of context, resulting in explanations that are challenging to comprehend and act upon for different stakeholders. This paper advocates for a paradigm shift towards context-sensitive explainability, tailoring explanations to users’ specific needs and understanding promoting inclusivity and accessibility. We propose a novel context taxonomy and a versatile framework, “ConEX” for developing context-sensitive explanations using any state-of-the-art post hoc explainer. Our empirical user study highlights diverse preferences for contextualization levels, emphasizing the importance of catering to these preferences to build trust and satisfaction in AI systems. Our contributions extend beyond the theoretical realm, offering practical guidance for developing context-sensitive explanations that are tailored to the specific needs of diverse stakeholders. By embracing context-sensitive explainability, we can unlock the true potential of AI, fostering trust, transparency, and informed decision-making across various domains.