This workshop continues a series of events organized at the Advanced Visual Interfaces (AVI) conference, previously focusing on Big Data Applications (BDA) and Information Visualization Systems (IVIS). With AVI 2026, this line of work is reframed around Trustable, Reproducible, and Intelligent Information and Visualization Systems (TRI-IVIS) that explicitly support trust, reproducibility, regulatory compliance, and visual analytics in data-intensive environments. Its goal is to advance methods and systems for managing, computing, and analyzing large-scale and complex data by integrating Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and advanced visual interfaces in a principled and transparent manner. 

The workshop builds upon the conceptual foundations established by the AI2VIS4BigData Reference Model, introduced in earlier editions of this workshop at AVI 2016 and AVI 2020. This reference model provides a structured framework for coupling AI-driven analytics with interactive visualizations, with particular emphasis on explainability, traceability, and trustworthy data exploration—key prerequisites for reproducible and regulation-aware information systems. 

An exemplar focus of the AVI 2026 edition is on, but is not limited to, genomic applications for AI-supported laboratory diagnostics. This domain is particularly suitable as it concentrates many of the workshop’s core challenges, including highly heterogeneous and large-scale data, long-term and distributed data storage, multi-stakeholder usage scenarios, knowledge discovery, user perception, ethical constraints, and regulatory requirements. At the same time, the workshop explicitly encourages cross-domain transfer of methods and models.

The target audience is mainly composed of researchers and practitioners working in AI and ML, Big Data analysis, and IVIS. The workshop targets application domains in which large-scale, heterogeneous, and semantically rich data must be interpreted and communicated in a trustworthy and reproducible manner, including healthcare, life sciences, digital humanitities, digital hermeneutics, scientific publications as well as meetings, incentives, conferences and events (MICE) management services. Contributions from related disciplines such as philosophy (e.g. ethics, trust, and interpretability) and psychology (e.g. perception and cognitive aspects of visualization) are strongly encouraged. 

Participation from the genomic and bioinformatics communities is also welcome. In this respect, the workshop can be seen as a continuation of the IEEE BIBM 2025 Workshop on Genomic Foundation Models for Diagnostic Innovation, held in Wuhan on December 15, 2025, while broadening its scope toward visual analytics, AI-enabled decision support, reproducibility, and cross-domain regulatory-aware information management.