Ideas from the speakers
The session emphasised that effective measurement of biodiversity is essential for making real progress. Unlike carbon, biodiversity is more complex and multidimensional, which makes it harder to measure in a standardised way, but still just as important. Reliable and comparable data across regions and over time is key for tracking progress, informing policy and supporting coordinated global action. In this context, artificial intelligence (AI) was highlighted as a valuable tool that can improve data collection, for example through satellite imagery, acoustic monitoring and automated image analysis. AI can also help fill gaps where data is incomplete.
Another important theme was how AI can transform biodiversity monitoring systems. Current approaches are often fragmented, resource-intensive and rely on limited expert capacity. AI has the potential to integrate different types of data, such as satellite imagery, field observations and scientific research, into more complete and scalable insights. However, it was emphasised that AI should support, not replace, human expertise. The most effective approaches combine automated analysis with human oversight, ensuring that results remain accurate, credible and useful. This also reflects a shift in the role of experts towards interpreting findings, validating results and guiding decision-making. The session also explored the economic dimension of biodiversity. For meaningful large-scale change to happen, biodiversity needs to be incorporated into financial systems and decision-making processes. This involves moving from raw data to actionable insights, then to indicators that can influence investment and policy. While current market mechanisms have their limitations, aligning economic incentives with positive environmental outcomes is seen as essential. AI can help accelerate this process by turning large amounts of data into usable insights, but human values and governance will ultimately shape how these tools are applied.
Overall, the discussion highlighted that progress depends on combining technological innovation, human expertise and broader economic change.
Insights from the audience
The discussion highlighted several important considerations around how AI is used in biodiversity contexts. A central concern was how to ensure that these systems are guided by appropriate values. Participants noted that current economic structures often prioritise short-term financial gains, sometimes at the expense of environmental and social outcomes. This raised questions about how new tools and frameworks can be designed to support a more balanced approach that takes ecological, social and economic priorities into account.
Another key point focused on trust and inclusivity. There was strong interest in understanding who contributes to and validates AI-generated outputs. Participants emphasised the importance of involving a diverse range of contributors, including local and indigenous communities, whose knowledge is often underrepresented in formal datasets. Including these perspectives was seen as essential both for improving data quality and for building trust and acceptance in new systems.
The potential for wider public participation also emerged as an important opportunity. Many participants noted that people already interact with nature and could contribute useful observations if accessible tools were available. The idea of user-friendly digital platforms, such as mobile apps that allow geolocated data collection, was discussed as a way to expand data coverage and support large-scale monitoring. At the same time, this raises challenges related to data reliability, uneven participation and possible bias towards more visible species or locations. Participants also reflected on the balance between scale and accuracy. While AI makes it possible to process large amounts of data, concerns remain about the quality and representativeness of that data. This reinforces the need to combine automated systems with expert validation and thoughtful system design.
Overall, the discussion showed that while technological capabilities are advancing quickly, the main challenges lie in governance, inclusivity, trust and turning data into meaningful action.
