Integrating AI and Transdisciplinary Approaches to Combat Climate Change

Transdisciplinarity integrates multiple perspectives and incorporates the “hidden third,” a concept introduced by Basarab Nicolescu.

The “hidden third” mediates between different viewpoints, creating a space where new, innovative solutions can emerge. Nicolescu emphasizes that transdisciplinary approaches are essential for sustainable development projects that require inputs from natural sciences, social sciences, and local communities. Moreover, he highlights the importance of integrating spiritual insights with other knowledge systems and disciplines – something important to AwhiWorld’s own practice.

This spiritual dimension fosters a deeper connection with nature and the universe, which is crucial for addressing complex issues like climate change, particularly in locations such as New Zealand, where Indigenous and other practitioners bring multidimensional viewpoints that are culturally critical and repeatedly disavowed and colonised.

Transdisciplinary methods often lead to solutions that do not neatly fit into predefined categories, promoting innovation and unconventional thinking.

Transdisciplinary methods transcend traditional disciplinary boundaries, engage a broader range of stakeholders, and consider spiritual and cultural dimensions, leading to more holistic, innovative, and sustainable outcomes. That’s why transdisciplinary approaches to climate change are, in our opinion, the most effective option.

That said, its important to note that each approach—interdisciplinary, multidisciplinary, and transdisciplinary—has unique strengths. (They are also contested terms presented here at a very basic level) Understanding each approach’s strengths and considering different contexts and specific projects is critical.

Despite AI’s potential benefits, several barriers make it difficult for grassroots and community groups to use this technology. We must address these challenges to help these groups fully benefit from AI.

 

One major barrier is the need for more access to advanced technology. High costs and the need for specialised skills make it challenging for grassroots organisations to get and use AI tools. Increasing funding and support, such as financial help and subsidies, can make it easier for these organisations to use AI effectively.

 

Data accessibility and quality are also big challenges. Due to limited resources and infrastructure, many grassroots organisations need help collecting, accessing, and managing the data they need. Creating open-access data repositories and improving data-sharing systems can provide the data required for AI applications, ensuring these organisations have access to high-quality information.

 

Ethical and privacy concerns are also an issue. Fears about data misuse, surveillance, and bias in AI algorithms make people hesitant to use these technologies actively. Creating ethical guidelines and privacy standards for AI helps build trust and use it responsibly, which addresses fears. Additionally, giving people the knowledge and skills to understand and deal with risks is essential.

 

Interoperability and integration challenges are also significant. Ensuring new AI technologies work well with existing systems and data sources is often complex and costly. Improving technical infrastructure by investing in internet connectivity and compatible hardware and software is crucial for the effective use of AI.

 

Technical expertise and training are critical issues, but overcoming these challenges can have huge benefits. Equipping community members with the necessary skills through training programmes and workshops on AI and data science can empower these groups to maximise their impact and fully benefit from AI.

 

Unfortunately, avoiding AI due to these potential issues is becoming increasingly complex, as it is being integrated into nearly every aspect of our lives, often without consumers having a choice. Rather than trying to avoid AI altogether—which will soon be almost impossible—it is more useful to understand the risks and build personal agency to navigate its use responsibly.

Transdisciplinary approaches to climate change using AI are a real opportunity to leverage a societally changing technology with an approach that is likely the most effective way to deal with this incredibly complex issue. The next step is to change attitudes and take action.

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Klein, J. T. (2014). Discourses of transdisciplinarity: Looking back to the future. Futures, 63, 68-74. ScienceDirect Link

Nicolescu, B. (2012). Transdisciplinarity: The Hidden Third, Between the Subject and the Object. Human and Social Studies, 1(1), 13-28. Link

North Carolina State University. (2020). The Difference Between Multidisciplinary, Interdisciplinary, and Convergence Research. Research Development Office. Link

Research Toolkit for Librarians. (n.d.). Multidisciplinarity, Interdisciplinarity, Transdisciplinarity. Oklahoma State University Library Link

Rigolot, C. Transdisciplinarity as a discipline and a way of being: complementarities and creative tensions. Humanit Soc Sci Commun 7, 100 (2020) Link

Utrecht University. (n.d.). Multi-, inter-, and transdisciplinarity; what is what? Utrecht University. Link

Utrecht University. (n.d.). What is transdisciplinary research? Utrecht University. Link

All images co-created with Midjourney