Explainable artificial intelligence (XAI) has become a central methodology for developing transparent, accountable, and human-centered AI systems. As data-driven models are increasingly deployed in high-stakes and socially consequential settings, explanations are expected not only to illuminate model behavior but also to support validation, error analysis, fairness auditing, regulatory compliance, and effective human–AI collaboration. This Editorial introduces the Special Issue “Explainable Artificial Intelligence Technology and Its Applications” and situates its contributions within the broader trajectory of XAI research. We briefly review major methodological families, including intrinsic interpretability, local surrogate and Shapley-value explanations, gradient- and perturbation-based visual attribution, counterfactual and causal explanations, and human-centered evaluation. We then highlight representative contributions in this Special Issue, which demonstrate how XAI is moving from generic explanation visualizations toward domain-sensitive, data-aware, and operationally reliable methods in network security, computer vision, knowledge graphs, and financial decision support. Finally, we discuss future directions, emphasizing faithful and plausible explanations, causal and multimodal reasoning, real-time and hardware-efficient deployment, trustworthy governance, and the emerging role of XAI in education.
Xuewen Sun, Lan Tian, Weidong Zhou et al.· Applied Sciences· 0 citations
While Multimodal Large Language Models (MLLMs) have demonstrated the capacity for multi-modal reasoning, current Referring Expression Comprehension (REC) benchmarks lag behind, predominantly relying on intra-image cues and neglecting the integration of external world knowledge, which significantly impedes the evolution of REC towards real-world applications. This limitation obscures a model’s true capability to conduct textual reasoning (entity resolution), resolve spatial location (visual grounding), and verify reference validity (hallucination rejection). To address this, we introduce KnowDR-REC, a targeted audit benchmark comprising 1,042 positive triplets derived from real-world knowledge, along with rigorously matched negative samples. Unlike traditional datasets, we implement a controllable counterfactual evaluation mechanism that subjects textual expressions to single-factor perturbations (entity, relation, or time) to test sensitivity to fine-grained factual changes. Extensive evaluation of 18 state-of-the-art MLLMs exposes a critical “binding hallucination,” revealing that current high performance is often built on fragile visual shortcuts rather than true understanding. KnowDR-REC thus serves as a pivotal diagnostic instrument, steering future research toward the genuine integration of perception and reasoning.
Guanghao Jin, Jingpei Wu, Tianpei Guo et al.· Annual Meeting of the Associ...· 0 citations