As part of ongoing policy development, NFSA teams collaborated on establishing clear principles regarding the use of machine learning (ML) and artificial intelligence (AI) in our daily practice.
At the National Film and Sound Archive of Australia, the creation and use of machine learning and artificial intelligence technologies are guided by three key strategies: building effectively and transparently, maintaining trust and creating public value.
From the 1930s to the present, the NFSA and its predecessor institutions have worked on and with emerging media technologies. From nitrate film to digital cinema production and streaming; from wax cylinders, shellac discs and wire audio recordings to LPs, CDs and podcasts; or from video games encoded on cassette tape to hand-held consoles and now VR – the technical evolution of audiovisual media creation, consumption and preservation is at the core of our work.
The National Film and Sound Archive of Australia Act 2008 requires us to develop, preserve, maintain and share the collection with all Australians – our core responsibility as a trusted custodian of Australia’s audiovisual heritage cannot be understated. As machine learning technologies and generative artificial intelligence systems proliferate within the screen and audio production ecosystems, we have taken an informed and critical approach to these technologies. We seek to develop technologies that help people find, understand and manage the NFSA’s collections to assist us in fulfilling our statutory responsibilities.
AI has the potential to unlock our vast collection of digitised and born-digital audiovisual material and help transform the NFSA. By increasing the discoverability and accessibility of the collection, AI can significantly improve the efficiency, accuracy and impact of our archival work. However, as an emerging technology and field of practice, AI also presents new risks and challenges that we must proactively manage. The challenge to responsibly deploying these technologies within a cultural archive is threefold: technical, cultural and legal. The following strategies frame our response to these challenges.
We maintain the trust of the public, creators, collection contributors and NFSA staff by ensuring AI projects work within our collecting policies while paying particular care with First Nations collections and seeking consent from stakeholders and copyright owners where required. We work within the relevant Australian laws, monitor anticipated changes in laws and regulations and actively contribute to the evolution of the legal and copyright environments in which we work. We declare the use of AI and AI-enabled services and contribute to AI literacy and responsible development at the NFSA and beyond.
We build and train machine learning systems ourselves so that they can understand Australian content and context. Where appropriate, we mix commercially available products with products built in-house, working experimentally and iteratively. We evaluate systems according to technical quality, cost and fitness, cultural safety and accuracy. We work transparently within the NFSA and collaboratively with external peers and partners, sharing what we learn.
Our use of ML and AI improves the discoverability and usability of our collections for everyone while developing AI systems that 'understand Australian' so they can engage directly with our culture. We invest in creating localised ML models for transcription and discovery to use and share with peers in an Australian context.
These principles will evolve as we learn through an iterative process, just as ML and AI technologies and uses evolve. We welcome feedback to ensure we continue to responsibly and thoughtfully use AI and ML technologies as we strive to share Australia’s national audiovisual collection.
The NFSA is a statutory authority established by the National Film and Sound Archive Act 2008 (Cth), and the development and use of AI and ML technologies is guided by the NFSA Act in the ‘endeavour to make the most advantageous use of the national collection in the national interest’ [s6(3)(b)] and to ‘promote the efficient, effective and ethical use of public resources’ [s6(3)(d)]. Visit the Governance section of our website for more information.
In our development and use of AI and ML technologies, we are also informed by the Australian Government’s Artificial Intelligence Ethics Principles and the National framework for the assurance of artificial intelligence in government.
Some NFSA staff use the Wasabi AiR media management platform for machine learning-supported content discovery tasks, and some use machine learning-enabled copy-proofing software for marketing and corporate communications. The NFSA is developing a set of custom transcription, summary, entity recognition and content discovery tools in-house that are specifically suited to NFSA collection content and internal workflows.
In October 2024, the NFSA is hosting Fantastic Futures, the annual gathering for the international AI4LAM (AI for Libraries, Archives and Museums) community. This event draws leading technologists, academics and cultural workers together to engage, share ideas and showcase their work.
NFSA Principles for Machine Learning and Artificial Intelligence Creation and Use (PDF)
The NFSA's approach to sustainability is guided by the First Nations principle of custodianship of Country.
The NFSA is proud to present Nangamai ('Dream' in Dharawal language), a collection of online First Nations content.
Guest First Nations writer Nathan Sentance offers suggestions about how historical collecting institutions can provide access to racist collection material without causing harm.
The National Film and Sound Archive of Australia acknowledges Australia’s Aboriginal and Torres Strait Islander peoples as the Traditional Custodians of the land on which we work and live and gives respect to their Elders both past and present.
