XAI for learning: Narrowing down the digital divide between “new” and “old” experts

Abstract

Regular eXplainable AI (XAI) approaches are often ineffective in supporting decision-makers across domains. In some instances, it can even lead to automation bias or algorithmic aversion or would simply be ignored as a redundant feature. Based on cognitive psychology literature we outline a strategy for how XAI interface design could be tailored to have a long-lasting educational value. We suggest the features that could support domain-related and technical skills development this way narrowing the digital divide between “new” and “old” experts. Lastly, we suggest an intermitted explainability approach that could help to find a balance between seamless and cognitively engaging explanations.

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