The rapid advancement and widespread deployment of Artificial Intelligence (AI) models, particularly deep neural networks, have led to remarkable successes across diverse domains. However, the inherent "black-box" nature of many high-performing models poses significant challenges, including a lack of transparency, trust, and accountability. Explainable Artificial Intelligence (XAI) aims to bridge this gap by making AI decisions understandable to humans. While numerous XAI methods have emerged, a crucial aspect often overlooked is the diverse and context-dependent nature of user preferences for explanations. A generic explanation may not suffice for all users or all decision-making scenarios. This article proposes a conceptual framework centered around a mapping function designed to adapt explanation generation to specific user profiles, contextual factors, and AI model characteristics. We review the landscape of XAI, analyze the varying needs of stakeholders, and detail the proposed mapping function's inputs, logic, and outputs. This user-centric approach promises to enhance the utility, trustworthiness, and effectiveness of XAI systems, fostering broader adoption and responsible AI deployment. We conclude by outlining key challenges and future research directions necessary to realize this vision.

With the rapid advancement of face recognition systems, security threats posed by adversarial attacks have become increasingly sophisticated. This study presents a novel adversarial methodology for crafting dual-identity face impersonations using Generative Adversarial Networks (GANs). The proposed framework generates synthetic facial images that simultaneously resemble two distinct target identities, thereby enabling high-confidence impersonation across multiple recognition systems. Leveraging a multi-objective loss function, the generator is trained to optimize both identity similarity scores and realism metrics while evading detection from spoofing and liveness classifiers. Extensive evaluations on benchmark datasets such as LFW and CASIA-WebFace demonstrate the effectiveness of the method in deceiving state-of-the-art face verification models with minimal perceptual distortion. The research highlights the vulnerabilities of current biometric systems and underscores the urgent need for robust defense mechanisms against such dual-target adversarial threats.

The push for inclusive language has gained global momentum, recognizing its crucial role in fostering respectful and equitable communication. While English has seen significant advancements in AI-assisted inclusive writing, highly gendered languages like Italian present unique challenges due to their complex grammatical structures, where gender is intrinsically linked to nouns, articles, adjectives, and verb participles. This article proposes a novel approach for developing an AI-powered system designed to assist in writing inclusive language within Italian formal communications. Our methodology integrates sophisticated Natural Language Processing (NLP) techniques, leveraging advanced Italian Language Models fine-tuned on expert-annotated datasets of formal texts. We outline the process of identifying non-inclusive patterns, generating grammatically correct and contextually appropriate inclusive alternatives, and the critical role of Explainable AI (XAI) to ensure user trust and understanding. Anticipated outcomes suggest that this system will significantly enhance the efficiency and accuracy of inclusive writing in Italian formal settings, promoting broader adoption of gender-neutral and person-first language principles.

Recommender systems are integral to the success of modern e-commerce platforms, guiding users to products and services that align with their preferences. While traditional systems often rely on past purchase behavior or content similarity, the increasing ubiquity of location-based services presents a significant opportunity to infuse geographic context into recommendation logic. This article presents a comprehensive overview of how geographic information, particularly in relation to population characteristics, can enhance e-commerce recommender systems. We explore methodologies for integrating spatial data, discuss the architectural implications, and analyze the benefits and challenges of developing location-aware recommendation strategies. Our review synthesizes existing research on point-of-interest (PoI) recommendations, location-based services, and geospatial information systems (GIS) within e-commerce, highlighting the potential for hyper-personalized experiences and localized business growth. We conclude by outlining key research gaps and future directions for leveraging geographic and demographic data to optimize e-commerce recommendations.

As Deep Neural Networks (DNNs) become increasingly integrated into critical systems—from healthcare diagnostics to autonomous vehicles—their vulnerability to malicious attacks has emerged as a serious security concern. Among these threats, Trojan attacks pose a unique risk by embedding hidden triggers during training that activate malicious behavior during inference. This paper presents a comprehensive life-cycle perspective on the security of DNNs, examining vulnerabilities across model development, training, deployment, and maintenance stages. We systematically categorize Trojan attack vectors, analyze real-world case studies, and evaluate the efficacy of current defense mechanisms, including pruning, fine-tuning, input filtering, and model certification. Furthermore, we propose a proactive framework for embedding security at each stage of the DNN life cycle, aiming to guide researchers and developers toward more resilient AI systems. Our findings highlight the importance of integrating security as a design principle rather than a reactive afterthought.