In the ever-evolving realm of artificial intelligence, the quest for transparency has led to the emergence of Explainable AI (XAI). This approach aims to make AI systems more understandable by elucidating the reasoning behind their decisions. A recent meta-analysis by Felix Haag delved into how XAI-based decision support affects human task performance. The study found that while XAI can enhance performance, the explanations themselves aren't always the primary driver of this improvement. Instead, factors like the risk of bias in studies play a more significant role. This insight underscores the complexity of human-AI interactions and the need for a nuanced understanding of how explanations influence decision-making processes.
Further research by Max Schemmer and colleagues examined the utility of XAI in human-AI decision-making. Their findings indicate a positive impact of XAI on user performance, particularly with text data. However, they also highlight that the type of explanation provided has a minimal effect compared to the AI's inherent accuracy. This suggests that while XAI holds promise, its effectiveness is closely tied to the quality and reliability of the AI system itself. These studies collectively emphasize the importance of a human-centered approach in designing XAI systems, ensuring that explanations are tailored to user needs and effectively integrated into decision-making processes.