NZCCM Conference · Companion Site
Introduction
Conservation professionals regularly work with concerns that are complex, often interconnected, and require clear communication for buy-in from colleagues, decision-makers, and the public.
Adding to this complexity, much of this work intersects with the competing priorities of others, requiring communication of layered data that is at once comprehensive yet not overwhelming.
This paper presents a series of explorations within the Auckland Libraries Conservation Team, in which AI has been used to create bespoke communication and training tools. These include interactive light exposure calculators; spatial air quality visualisations; and scenario-based disaster readiness games.
Conservation sits at an intersection between data-driven technical science and human-centred humanities practice: disciplines that broadly carry contrasting adoptions and objections to AI. The tools presented in this paper occupy a measured position: AI used to structure complexity, facilitate discussion, and support decision-making; a tool used to raise up, rather than displace, the specialist work we do.
The Approach
These tools have been created through an iterative design process, with foundational documents embedded, such as: design specs, general instructions, examples, and key references, (e.g. the NZCCM Code of Ethics).
While all of these tools utilise a web app format, coding with AI allows for a great variety of other uses. Sensitive data is a key concern, both in input to AI models, and when publishing to the web.
Prompting
The way that a first prompt is structured can help to shape the end product, supporting use of reputable, repeatable, and verifiable data. Open the framework below to explore each one.
Examples
Closing
The projects described in this presentation give an indication of directions that AI might allow you to explore. I haven't expanded on more detailed aspects of my workflow. Feel free to get in touch with me regarding any aspect of this work.