FIAT/IFTA World Conference 2026

Sub-Title: Building Together the Future of The Profession


In response to rapid changes in the audiovisual archive and media profession, the FIAT/IFTA is working on the project of a Think Tank for education in future skills and consolidation of shared knowledge.
 
As a matter of fact, technological transformation, automation and digital workflows are reshaping roles, skills and professional identities, while some traditional knowledge - especially related to analogue and manual practices - is at risk of being lost.
 
The Think Tank missions are to:

• Monitor global trends affecting the profession.
• Anticipate skills gaps and emerging needs.
• Act as a strategic observatory for professional evolution in audiovisual archives and related fields.

To carry out these missions, it should actively establish and strengthen alliances and partnerships, and ensure diversity of expertise, professional backgrounds and geographic representation through a dedicated governance. 
It will then launch its first activities, at the service of the professional community.
 
This Discussion panel aims to present and discuss emerging talking points and invite the audience to add to our story, through interactive polls.
 
It will bring together 4 participants (names to be confirmed):

• A representative of the FIAT/IFTA
• A representative of the training and education sector
• A representative of a broadcast/film archive of the south american region
• A representative of a regional archive holding TV, Radio and/or Film collections

If possible, we think that a plenary session would allow to highlight the need for ongoing education on all levels in archives, no matter the size and scope.

In the high-pressure environment of major broadcasting, the demand for archival media is defined by speed, precision, and instant availability. At Globo, managing decades of rich audiovisual heritage across Journalism, Sports, and Entertainment requires a constant evolution of archival strategies. The Globo Mídia project represents the latest strategic leap in this journey, designed to amplify the reach and agility of our media ecosystem through advanced data structures.


The core of Globo Mídia lies in the implementation of ontologies that structure vast amounts of media data, providing a unified semantic language across the company. This framework provides the necessary context for AI-driven initiatives, ensuring that technology acts as a powerful ally in metadata extraction and information retrieval. By creating these intelligent connections, Globo Mídia ensures high-speed discovery and accuracy, even in the most demanding and time-sensitive production scenarios.


The ultimate goal of this technological advancement is to liberate the researcher’s eye. By using the Globo Mídia ecosystem to automate mechanical indexing and repetitive tagging, we empower our specialists to transcend labor-intensive tasks. This shift allows the human researcher to focus entirely on their role as a strategic content partner and curator, applying historical sensitivity and deep creative insight to the development of new products.


At Globo, we believe that technology should handle the scale and complexity of the data so that human intelligence can drive the storytelling. We will demonstrate how Globo Mídia bridges the gap between massive archival collections and high-demand production needs, ensuring that our historical assets are instantly accessible to fuel future creativity and narrative excellence.

Early 20th-century archaeological expeditions produced a rich body of visual material: film footage, field photographs, lantern slides documenting landscapes, excavation methods and cultural contexts. Dispersed across museums, university libraries and national film archives, these film collections remain difficult to discover, cross-reference or integrate into contemporary research. This presentation reports on an ongoing project that applies AI techniques to support descriptive cataloguing and access in digitized early film archives.
The project centers on a corpus of records from institutions in Europe and North America. Using vision language models, it explores how AI-assisted workflows help integrate contextual metadata, including site, estimated date ranges, artefact types. The presentation goes through opportunities and challenges of existing models trained on modern visual conventions that differ from historical image quality, aesthetic and stylistic composition. It observes how black-and-white historical footage with inconsistent framing and labels requires domain-specific approaches, and which solutions can be envisaged to preserve the richness of early audiovisual information.
Beyond cataloguing, the presentation examines how automated archival description affects and transforms cultural memory. Text matches and visual similarity now allow researchers to reconstruct early film sequences across archives and curate thematic collections that reframe and reposition our understanding of historical events. The paper reflects critically on the digitization of early footage and its entanglements with automation: which screen memories can machines unlock?
By sharing epistemological reflections on early documentary film culture, this presentation aims to contribute to broader conversations in the FIAT/IFTA community about the responsible integration of AI into archival workflows and the renewed relevance of historical film collections as forms of cultural mediation.

During the 2025 Club World Cup, Globo’s Archive department, in partnership with the Sports Editorial team, conducted a proof of concept (PoC) using Google Vision Warehouse to explore new possibilities for content discovery and usage in live production environments.
 
The initiative focused on evaluating how artificial intelligence can transform the way content is retrieved and utilized in sports journalism and live broadcasting.
 
By leveraging automated video processing, the solution was able to interpret specific contexts and situations within the footage. This enabled a shift from traditional keyword-based searches to a contextual approach, significantly expanding the scope of content discovery.
 
In practice, the Sports team gained the ability to access content through contextual queries—such as specific game situations, crowd behavior, or visual elements—even when this information had not been previously indexed in the metadata. This new model increased the speed of content retrieval, added depth to editorial coverage, and enabled the discovery of previously underutilized assets.
 
The PoC demonstrated the strategic potential of AI within the Archive, making content more accessible, reusable, and relevant across diverse editorial contexts.

This presentation reflects on how a practical archival task inside a Uruguayan television broadcaster evolved, over sixteen years, into a knowledge-driven system for managing, contextualising and reusing audiovisual memory.
The project began in 2010, when I was assigned the recovery of a historical television archive using only handwritten paper notebooks as the primary access tool. At the same time, much of the collection still existed on ageing analogue and early digital videotape formats (1-Inch Tape, U-Matic). To make the material accessible, I had to learn through experimentation how to clean, restore and digitise playback machines that were already several decades old.
Coming from a generation that experienced both analogue video culture and the first stages of digital editing, I combined broadcast operational knowledge with self-taught experimentation. Early attempts using Word documents and spreadsheets quickly proved insufficient for the speed and precision required in daily television production. This operational pressure led me to begin learning programming and databases in order to recreate the logic of the paper workflow in digital form: locating content quickly, identifying the correct tape, and later the precise timecode inside the material, find actors etc.
As newsroom demands increased, the system evolved continuously through real-world use, driven by requests from colleagues who relied on rapid access to historical footage. Even after the archive department was closed in 2020, the project continued independently, incorporating long-term preservation logic, production workflows, contextual metadata, artificial intelligence support and large-scale efficiency testing using a personal dataset of more than one million media assets collected over years of archival practice.
This case study explores how necessity, situated learning and broadcast reality can generate technological innovation from within archival work itself.
That's my story.

Artificial intelligence has rapidly become an important tool in contemporary film restoration. Machine learning systems now assist with chroma reconstruction, density balancing, spatial inference, defect detection, and reference-guided restoration. Despite these advances, current AI methods still encounter significant limitations when dealing with the physical and statistical characteristics of photochemical film and scanned archival materials. This presentation examines the principal technical boundaries of present-day AI restoration systems and identifies the research directions most likely to overcome them. The first major limitation is the loss of high-frequency information: many generative models operate in compressed latent representations that suppress stochastic micro-signal, producing over-smoothed textures and synthetic noise patterns. The second limitation is temporal consistency: motion picture restoration requires stable behaviour of fine detail across frames, yet current systems often produce flickering textures and motion-dependent smoothing. The third limitation concerns acquisition-specific signal characteristics: film stock response curves, dye fading behaviour, scanner optics, and laboratory processing artifacts vary enormously across archival materials. The presentation then examines emerging approaches that may address these limitations. Pixel-space diffusion offers improved microstructure reconstruction but at extreme computational cost. Wavelet-based diffusion frameworks reorganise image information into frequency bands while preserving spatial localisation. Grain transport - treating film grain not as noise to be removed but as a structured stochastic signal to be probabilistically preserved - represents a further frontier. Drawing on the presenter's ongoing experimental work in machine-learning-assisted film restoration, the presentation argues that the most promising path forward lies in hybrid workflows that combine machine learning inference, classical frequency-domain processing, stochastic grain modelling, and expert human supervision.

The Images Sport AI Project is the second major phase of the IOC’s long‑term digital transformation of its archival ecosystem, following the successful migration from bespoke on‑premises systems to a cloud‑based, integrated architecture between 2021 and 2024. This foundation introduced seven interoperable platforms for preservation, access and analytics, and enabled the structured migration of more than 85,000 audiovisual assets and 928,000 photographs with full metadata. 
Launched in 2024 in alignment with the IOC AI Agenda, the Images Sport AI Project introduces state‑of‑the‑art artificial intelligence into both front‑end discovery and back‑end documentation workflows. Its dual objective is to enhance search capabilities in The Olympic Media Library (TOML) and accelerate metadata creation within the Media Asset Management system through automated tagging. 
Front‑end innovations include AI‑driven image and video similarity search, enabling users to retrieve visually related content that traditional text‑based descriptions cannot capture. A conversational search prototype extends this further by combining TOML metadata, multimodal search and IOC data, allowing natural‑language queries and richer discovery pathways. 
On the back end, automated tagging uses deep‑learning models are developed to generate sport‑specific vocabularies (actions, equipment, venues), general actions, logos, symbols and framing descriptors, while deliberately excluding athletes’ identities or emotions to ensure compliance with legal and ethical frameworks. Winter sports vocabularies form the basis for pilot deployments ahead of Milano Cortina 2026, with Summer sports following  for Dakar 2026 Youth Olympic Games and Los Angeles 2028 Olympic Games. 
The project directly addresses key questions about large‑scale infrastructure, automation, authority control and ethical metadata governance. It positions the IOC Archives as a global laboratory for innovation within Sport Archives, demonstrating how AI can strengthen discoverability, operational efficiency and long‑term stewardship across extensive heritage collections.

This paper presents the development of a high-speed video search system based on image features for a large-scale video archive, NHK Archives, without relying on manually created metadata. Conventional video retrieval systems typically require extensive manual metadata annotation. In contrast, the proposed system extracts still images from video clips and computes their features using the CLIP model, enabling multimodal search with text or images to retrieve semantically related scenes.
To achieve practical response times over a massive dataset, the system employs approximate nearest neighbor (ANN) search. In addition, a prototype function was developed to automatically generate program structures by combining speech recognition and large language models (LLMs), improving the interpretability of search results by clearly indicating the corresponding programs and scenes.
The system adopts a hybrid architecture in which computationally intensive processes—such as clip extraction, CLIP feature computation, and speech recognition—are executed on-premise, while structure generation and search operations are performed in the cloud. This approach significantly enhances the accessibility of video content while reducing the cost of metadata creation for large-scale video archives.

We present an initial methodological framework for representing events in the automated cataloguing of journalistic audiovisual content. Integrated into a broader research project on the automation of document analysis in newsrooms, this study examines how eventbased metadata can be effectively modelled using controlled vocabularies, without requiring structural modifications to existing MAM indexing
systems. The approach operationalizes the 5W model—adapted here as 4W + 1H—through lightweight, nonontological solutions suitable for legacy documentation environments.


The research draws on a corpus of more than 5,000 news items related to the 2024 DANA weather event in the Valencian Community. Using a representative sample, the study analyses three types of documentary elements—documents, segments, and stories—to identify common metadata patterns, assess their potential for reuse in cascading automated workflows, and determine the structural limits of storylevel aggregation in news production contexts.


The results reveal persistent issues such as overindexing, multisemantic fields, weak traceability, and inconsistent responses to the 5W framework. The study examines whether advanced semanticweb structures (ontologies, knowledge graphs) are necessary, or whether enhancing existing controlled vocabularies and refining metadata field architectures can provide sufficient robustness and operational value.


The findings underscore the need to improve metadata structures whose semantics depend both on their formal design and on their functional placement within the document record, as key factors for enhancing retrieval, consistency, and automation in audiovisual news cataloguing.

The Archive establishes itself as a living space for creation, innovation, and value generation. In a landscape driven by artificial intelligence, a new form of leadership emerges: the Archive professional who also acts as a content producer, strategic curator, and connector within the media ecosystem.
With the support of AI, knowledge that was once built exclusively through manual descriptions expands. Technologies begin to uncover relationships, contexts, and opportunities hidden within content. This professional understands the Archive as an integral part of the media production process.
All of this happens without losing what is essential: preserving valuable material, caring for raw footage, maintaining organization, and defining what should be kept as heritage and collective memory.
The future of the Archive does not break with its foundations—it amplifies them. And it positions this professional as a bridge between memory, technology, and the creation of new futures.

Since 2023, the Caracol Television audiovisual archive has been working on the technical verification and digitization of its 16mm film collection. This includes more than 3,000 newsreels and program rushes that remained unseen or unrecognizable for generations of viewers for over 50 years.
In partnership with the Faculty of Arts at Javeriana University, we have been working on an unprecedented process: digitization using a Gugusse Roller, a "handmade" technique that has allowed us to uncover images of our country from the 1960s and 70s. With the support of visual arts students, we have begun a training program that encompasses all stages of working with this type of material: verification, preparation, digitization, digital preservation, and licensing for creative projects.


This presentation will not only describe the details of the process but will also highlight the use of open-source technology to overcome the lack of a scanner. In addition, we will reflect on the importance of creating pedagogical processes for creative uses of the archive in our context.

Artificial intelligence is rapidly becoming a primary interface to knowledge and culture. Audiovisual archives therefore cannot remain passive observers. They steward some of the richest records of public life: broadcast history, oral testimony, performance, news, memory and cultural expression. As AI systems increasingly mediate discovery, interpretation and reuse, archives face shared challenges around extraction, invisibility, misrepresentation, governance and reciprocity.
This panel frames these challenges through the concept of Public AI: an approach to AI that serves the public interest rather than purely commercial logics. For audiovisual heritage, this means three concrete things. First, public assets: archival data, metadata, tools and standards should remain as open, interoperable and reusable as possible, reducing dependence on closed platforms. Second, public purposes: AI should support access, interpretation, education, participation and creative reuse of audiovisual heritage, rather than merely mining archives as raw material. Third, public oversight: decisions about how archival content is used in AI, under what conditions, and with what safeguards, must remain subject to meaningful public accountability.
Together, these principles offer a practical alternative to extractive AI models. They position audiovisual archives not just as data providers, but as institutions that can shape more democratic and trustworthy AI futures.
In this panel, three speakers will explore how audiovisual archives can contribute to Public AI through data stewardship, contextualisation, standards, rights-aware access and collaborative governance. The discussion builds on the Alignment Assembly “Culture for AI” [1] and follow-up work linked to the Data Space for Cultural Heritage and the European Collaborative Cloud for Cultural Heritage.
Participants will gain concrete insights into how audiovisual archives can respond strategically to AI, including practical perspectives on metadata, rights, access, standards and collaboration that are directly relevant to the FIAT/IFTA community.
[1] https://www.dataspace-culturalheritage.eu/en/news/discover-our-new-publication-on-artificial-intelligence-and-the-data-space

Audiovisual archives are sovereign infrastructures of cultural memory. They are not merely collections of content: they are the evidentiary foundation of public discourse, the raw material of historical knowledge, and a shared resource whose integrity is a condition of trust in media institutions. Generative AI and deepfakes can pose a structural threat to this integrity — not only by enabling the production of false content, but by corroding the epistemic status of archival material itself. A manipulated archive is no longer a reliable source; it is a liability.
This paper argues that broadcasters occupy a distinctive position in the governance of AI-generated deepfakes: neither as technology providers nor as distribution platforms, but as custodians of audiovisual heritage with editorial responsibility, reputational capital, and existing technical capacity. From this position, broadcasters can act as upstream governance actors — intervening at the point of content production rather than relying exclusively on downstream detection, fact-checking, or user-labelling. The central claim is that voluntary adoption of content-marking standards (watermarking, C2PA/IPTC metadata, provenance certification) by broadcasters constitutes a form of shared technological capacity that strengthens the infrastructure sovereignty of audiovisual archives.
The paper develops its argument through three moves. First, it situates broadcasters within a polycentric governance framework (drawing on Ansell et al., 2023 and Floridi et al., 2024, 2026), distinguishing this approach from the normative inflation critique: the effectiveness of broadcaster-led governance derives not from adding further voluntary principles to an already saturated landscape, but from the specificity of institutional incentives and the concreteness of available technical tools. Second, it analyses Article 50 of the EU AI Act and the draft Code of Practice on AI-generated content transparency (currently under finalisation) as a case study of how regulatory frameworks interact with — and depend upon — horizontal governance by media institutions. Third, it addresses the question of transferability: drawing on critical perspectives, the paper asks what conditions must be met for broadcaster-led marking standards to function as genuine shared capacity across diverse institutional and infrastructural contexts.The argument is grounded in a body of emerging literature on AI governance, media self-regulation, and archival provenance standards, and engages directly with initiatives such as the EBU's sovereign AI framework, and the guidelines developed by the Archival Producers Alliance. 

When Reuters News Agency acquired the commercial archive licensing operation Screenocean, it inherited two distinct media asset management (MAM) systems. Maintaining these parallel environments — each shaped by its own metadata schemes, cataloguing practices, and operational workflows — introduced growing risks to preservation, discoverability, rights management, and long‑term sustainability. 
The challenge was twofold: migrating a large, active audiovisual archive while preserving the integrity and context of its assets. This paper outlines the methodologies used to address this challenge, including metadata mapping, risk assessment, and workflow redesign. 
This presentation examines the unification of these MAM environments as a case study in preservation through migration. Although driven by technical necessity, the consolidation was equally an exercise in ensuring continuity of archival stewardship amid organisational and technological change. 
Beyond large-scale asset migration, the paper explores effective approaches to MAM consolidation, including metadata harmonisation, duplication management, and the preservation of contextual and rights information that give archival materials their meaning and value. 
The project offers a practical case study for archives facing merger, restructuring, or infrastructure renewal, highlighting key lessons related to preservation, access, and sustainability.