The impacts of AI on biodiversity and conservation

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  • Date
    3 June 2025
    Timeframe
    16:00 - 17:00 CEST Geneva
    Duration
    60 minutes
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    As the planet faces accelerating environmental degradation and biodiversity loss, emerging technologies have an increasingly vital role to play in enhancing conservation efforts. Artificial intelligence (AI), in particular, is emerging as a powerful tool to address complex environmental challenges, from monitoring ecosystems and predicting habitat changes to combatting illegal wildlife trafficking. 

    In this webinar, Professor Tshilidzi Marwala, United Nations Under-Secretary-General and Rector of the United Nations University (UNU), will outline the transformative potential of AI for biodiversity and conservation. He will examine how AI can support the ambitious targets of the Kunming-Montreal Global Biodiversity Framework by enabling scalable, data-driven solutions that enhance our ability to understand and protect our ecosystems. 

    Drawing on UNU’s global work at the intersection of AI and sustainability, Prof. Marwala will also reflect on the ethical, environmental, and equity-related challenges associated with AI, such as algorithmic bias, the digital divide, and its carbon footprint. Realising the promise of AI for biodiversity conservation will depend on our ability to govern it wisely, ensuring it serves as a force for equity, sustainability, and environmental stewardship.

    Learning Objectives: 

    By the end of this webinar, participants will have developed their ability to:

    • Identify key facing global biodiversity and conservation efforts.
    • Describe the potential applications and limitations of artificial intelligence in addressing these challenges.
    • Analyse real-world case studies illustrating how AI is being used to support biodiversity monitoring and conservation.
    • Evaluate the role of the United Nations University in advancing research and innovation at the intersection of AI, sustainability, and biodiversity.

    Recommended Mastery Level / Prerequisites:
    None