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talk · Tuesday 22 September · 08:57–⁠09:09 · SAL A

Delivering AI for biodiversity data in the public sector: data handling, security and humans-in-the-loop when small teams build at pace

Rachel Wiles · AI for Biodiversity Data

Recording time 13:34–28:27Open on Vimeo ↗

Summaries and key moments are machine-produced. Check the recording for the speaker’s exact words.

The short versionSmall public-sector teams can use AI tools productively if they set clear boundaries between prototype, dev, test and production and keep expert humans in the loop.

Overview

What this was about

Rachel Wiles, product manager for the Natural History Museum's Data Ecosystem (a biodiversity data platform for largely urban UK nature built with AWS), described how a very small tech team uses AI tools to build at pace. She set out three boundaries of AI use: Claude Design for UI prototyping with dummy data, AWS Kiro as a code assistant running within the museum's AWS account for governance and logging, and GitLab agents for automated code review before deployment. She listed team rules (no AI acting on production data, no promoting unrewritten prototype code beyond dev, never bending the data model to a mock-up) and described human-in-the-loop verification tools (duplicate detection, PII/human detection, species clustering) surfaced as suggestions to expert verifiers. She asked the community for ideas on validation and provenance.

Why it matters. Offers a candid, practical model of AI governance for under-resourced biodiversity data teams, and raises the open problem of preserving provenance and credit when AI assists data workflows.

Key ideas

In the room

  • The Data Ecosystem aggregates eDNA, environmental sensing, community science and camera trap data with quality checks; 10 million records in its first six months.
  • Built in a four-year AWS partnership by a tiny team (a data engineer, a senior engineer and a product manager) with consultants filling gaps.
  • Claude Design is used for wireframes and prototypes with generated dummy data, avoiding museum IP or real records.
  • AWS Kiro sits inside the AWS account, allowing central access control and logging of model use.
  • GitLab agents add automated code review before deployment.
  • Organisations without an AI policy should get one now; retrofitting code to a policy is hard.
  • AI-assisted outputs (duplicate detection, PII checks, species clustering) arrive as suggestions for Centre for UK Nature experts to action.
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Notable moments

In their words

Transcript

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Programme abstract

Delivering AI for biodiversity data in the public sector: data handling, security and humans-in-the-loop when small teams build at pace

Rachel Wiles

AI is reshaping how biodiversity data is collected, prepared and interpreted. It is also reshaping how the tools that do this work get built: practices loosely described as “vibe coding” or AI-assisted programming have changed what small teams can deliver, and how quickly. For public-sector bodies such as natural history museums, the opportunity is significant but the constraints are distinctive. Data governance, security baselines, accountability to research users, and the long-term stewardship of biodiversity records all shape what is possible. This talk offers a practitioner's perspective from the Natural History Museum, London, on applying AI in the development of biodiversity platforms in a public-sector context. It draws on experience building the AWS Data Ecosystem, the Museum's cloud-based platform for UK biodiversity data. The platform ingests biodiversity records gathered over time and streamed continuously from a range of sources, and serves research scientists as its primary user community. Two areas will be in focus. The first is data handling and security, including the questions raised when sensitive or pre-publication biodiversity data meets AI services in a public-sector environment. The second is human-in-the-loop design, and how to structure workflows so research scientists retain scientific authority over AI-assisted outputs while still benefiting from the productivity AI offers. The talk will also consider the financial and computational costs of AI-enabled approaches, particularly where cloud-based services are used operationally rather than experimentally. The talk is practice-oriented. It aims to share what is working, what is not, and where shared guidance from the TDWG community would help most.

Submitted to the conference programme before the talk, so the talk itself may differ.