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The unprecedented performance of machine learning models in recent years,particularly Deep Learning and transformer models, has resulted in theirapplication in various domains such as finance, healthcare, and education.However, the models are error-prone and cannot be used autonomously, especiallyin decision-making scenarios where, technically or ethically, the cost of erroris high. Moreover, because of the black-box nature of these models, it isfrequently difficult for the end user to comprehend the models' outcomes andunderlying processes to trust and use the model outcome to make a decision.Explainable Artificial Intelligence (XAI) aids end-user understanding of themodel by utilizing approaches, including visualization techniques, to explainand interpret the inner workings of the model and how it arrives at a result.Although numerous research studies have been conducted recently focusing on theperformance of models and the XAI approaches, less work has been done on theimpact of explanations on human-AI team performance. This paper surveyed therecent empirical studies on XAI's impact on human-AI decision-making,identified the challenges, and proposed future research directions.

Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making