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

Explanations alone don't guarantee better decisions: While explainable AI (XAI) helps users understand how AI models work, the paper emphasizes that simply providing explanations doesn't automatically improve human-AI team performance. The impact of explanations on actual decision-making outcomes requires careful empirical study rather than assumption.

High-stakes domains need human oversight: The paper identifies finance, healthcare, and education as critical application areas where AI errors carry significant technical or ethical costs. In these fields, AI systems cannot operate autonomously and must be paired with human decision-makers who can evaluate and act on AI recommendations.

The "black box" problem remains a barrier to trust: Deep learning and transformer models often cannot explain their reasoning in ways humans can understand. This opacity creates a fundamental challenge for users who need to comprehend why an AI reached a particular conclusion before trusting it enough to act on it.

Research gap exists between AI performance and human-AI collaboration: The paper notes that while many studies focus on improving model performance or developing XAI techniques, relatively few empirical studies examine how explanations actually affect human decision-making in real-world scenarios—highlighting an important area needing more research.

Visualization and interpretation techniques are key tools: XAI approaches using visualization and interpretation methods help bridge the gap between complex AI models and human understanding, making it possible for end-users to comprehend model outputs and the processes behind them.