Title: The Impacts of AI on Biodiversity and Conservation
Resource URL: https://www.youtube.com/watch?v=GBiphW0YmaY
Publication Date: 2025-06-03
Format Type: Video
Reading Time: 56 minutes
Contributors: Dave Thau;Tshilidzi Marwala;
Source: AI for Good (YouTube)
Keywords: [AI in Conservation, Biodiversity Loss, Data Sovereignty, Ethical AI Governance, Sustainable Development Goals]
Job Profiles: Data Scientist;Chief Sustainability Officer (CSO);Environmental Consultant;Artificial Intelligence Engineer;Chief Technology Officer (CTO);
Synopsis: In this video, WWF’s global data and technology lead scientist Dave Thau and United Nations Under-Secretary-General Tshilidzi Marwala explore artificial intelligence’s role in biodiversity monitoring, conservation strategies, and sustainable ecosystem management.
Takeaways: [AI can empower biodiversity conservation, but true impact depends on scalable, ethical, and inclusive governance structures shaped by human choices., Traditional conservation methods are no longer sufficient alone, meanwhile, AI offers scalable and adaptive tools but must be paired with behavior change., Indigenous knowledge and data sovereignty must be respected and integrated into AI systems to avoid repeating patterns of technological colonization., The environmental cost of AI, including energy and water use, must be addressed to avoid undermining its potential for sustainability., Cross-border data sharing is essential for managing biodiversity and climate issues that transcend national boundaries, requiring multilateral governance.]
Summary: The webinar opens by positioning AI for Good as a collaborative United Nations platform that advances partnerships and standards for tackling global challenges through artificial intelligence. Organizers emphasize that while sensor data analysis has a long history in conservation, the series will delve into critical enabling factors—ranging from socioeconomic data integration to ethical frameworks—required for truly impactful AI applications in biodiversity.

The keynote speaker outlines core drivers of biodiversity loss—habitat degradation, invasive species, pollution, and overexploitation—and illustrates these issues with a 69 percent global wildlife population decline since 1970 and a personal anecdote of species loss in his home village. He argues that traditional conservation tactics such as protected areas, species relocation, and legal safeguards lack scalability, flexibility, and responsiveness in the face of accelerating ecological change.

Demonstrating AI’s unique strengths, the talk covers large-scale analysis of audio and imagery, adaptive management inspired by genetic algorithms and reinforcement learning, and the deployment of drones for low-impact monitoring. Case studies span flood prediction in Benin, water-level forecasting in dam systems, anti-poaching initiatives in Uganda and Cambodia, habitat mapping in Costa Rica, and ecosystem restoration in Scotland.

The speaker presents United Nations University’s global AI network—over 100 member institutions—and describes projects in Tokyo, Germany, Namibia, Canada, Macau, and Beijing that address invasive species, water security, sustainable energy, and responsible AI governance. He acknowledges limitations including data quality gaps, algorithmic bias, financial and technical barriers in the Global South, and the hidden energy and water costs of AI systems.

Concluding, he calls for ethical frameworks and harmonized governance structures at international, national, and industry levels, with transparent accountability and inclusion of indigenous knowledge. Emphasizing that responsible AI begins with individual and organizational commitment, the speaker urges stakeholders to design efficient algorithms and policies that harness AI’s power to advance sustainable development and reverse biodiversity decline.
Content: ## Introduction

AI for Good is a collaborative initiative convened by the International Telecommunication Union, co-hosted with Switzerland and over forty United Nations agencies, to develop partnerships, skills, and standards that harness artificial intelligence to address critical global challenges. Participants are encouraged to engage actively via live video wall discussions, networking sessions, and various virtual exhibits.

## The AI for Biodiversity Webinar Series

The AI for Biodiversity series aims to stimulate creative solutions for monitoring, conserving, and sustainably using biodiversity through AI. While conservation organizations have long employed sensor data analysis, this series emphasizes the broader enabling conditions necessary for effective AI adoption—such as integrating socioeconomic information, protecting local data sovereignty, and applying ethical frameworks.

## Speaker Background

The session is introduced by the global data and technology lead scientist at WWF, an independent conservation entity operating in more than one hundred countries. The keynote is delivered by the Rector of the United Nations University and Under-Secretary-General of the United Nations, whose multidisciplinary research spans artificial intelligence applications in engineering, computer science, finance, social science, and medicine.

## Enabling Conditions for Impactful AI in Biodiversity

Effective AI deployment in biodiversity depends on understanding its global applications, embedding socioeconomic data into decision-making, securing data sovereignty—particularly for indigenous and local communities—and adopting privacy-preserving techniques such as federated AI. Ethical considerations and private sector integration are also essential.

## Biodiversity Loss: Drivers and Challenges

Global commitments, including the 2022 Kunming-Montreal biodiversity framework, target halting and reversing biodiversity decline by 2030 through the protection of 30 percent of Earth’s surface and the restoration of 30 percent of degraded ecosystems. Major drivers of biodiversity loss include habitat destruction, invasive species, pollution, disease, and resource overexploitation. A 69 percent average decline in wildlife populations since 1970 illustrates the severity of these trends.

## Limitations of Traditional Conservation Approaches

Conventional measures—such as establishing protected areas, relocating endangered species, and enacting legal safeguards—often lack the scalability, flexibility, and responsiveness required to address rapidly changing ecological conditions. Climate-induced extreme events, invasive species spread, and pollution further complicate these efforts.

## AI’s Contributions to Biodiversity Conservation

### Large-Scale Data Analysis

AI can process extensive audio, imagery, and remote-sensing data to detect pollutants, map habitats, and monitor species without direct human disturbance.

### Adaptive Conservation Management

Techniques inspired by genetic algorithms and reinforcement learning allow AI systems to iteratively adjust conservation strategies in response to new environmental data.

### Efficiency and Reduced Disturbance

Drones equipped with AI can survey remote or sensitive areas more effectively than ground patrols, minimizing ecological impact while expanding monitoring capacity.

### Predictive Modeling and Environmental Monitoring

Forecasting tools built on AI have successfully predicted floods in Benin, dam water levels and hazards, and regional water demand. Wind speed predictions support the development of renewable energy sites, contributing to both biodiversity protection and climate mitigation.

## Illustrative Case Studies

• Anti-poaching efforts in Uganda and Cambodia employ AI to prevent wildlife trafficking.
• Ecosystem restoration in Scotland uses AI to identify priority areas and track progress.
• Habitat mapping in Costa Rica leverages enhanced audio monitoring of spider monkeys.

## United Nations University Initiatives

The United Nations University’s global AI network encompasses over one hundred academic and research institutions. Key projects include an AI-driven invasive species pilot in Namibia, water security monitoring in Canada, whitefly management in Macau, and forthcoming institutes focusing on algorithmic research and governance in Beijing and Bologna.

## Governance, Ethics, and Responsible AI

Critical challenges include data quality and availability, algorithmic bias, financial and technical barriers—especially in the Global South—and the substantial energy and water footprints of large AI systems. Responsible governance requires ethical frameworks, transparent accountability, education to cultivate responsible human and organizational behavior, and harmonized policies, standards, and regulations at international, national, and industry levels.

## Conclusion

Harnessing AI’s analytic power and scalability can accelerate progress toward biodiversity targets and the United Nations Sustainable Development Goals. However, realizing these benefits demands committed human decision-makers, cross-sector collaboration, and the careful design of efficient, inclusive, and ethically governed AI systems to ensure that technology serves the common good.