Generative AI for Good: A Game Changer for NGOs in 2024?

Unlocking Potential Through Innovative Solutions

As we step into 2024, non-governmental organizations (NGOs) are increasingly turning towards generative AI to tackle some of their most pressing challenges. This burgeoning technology offers a plethora of innovative solutions, revolutionizing the way NGOs operate. At the same time, it comes with several challenges that need to be overcome for successful adoption. 

Transformative Use Cases for NGOs

• Data Analysis and Prediction: NGOs are leveraging AI to sift through vast datasets, predict trends, and pinpoint areas requiring urgent attention. This proves indispensable in sectors like public health, environmental monitoring, and humanitarian aid.
• Automated Content Creation: From drafting reports to crafting social media posts and educational materials, generative AI is a powerful tool for content generation, significantly conserving time and resources.
• Language Translation and Accessibility: With real-time translation capabilities, AI breaks down language barriers, crucial for international aid and working in multicultural settings.
• Personalized Donor Engagement: Tailoring communication with donors based on their interests and past interactions is made effortless, enhancing engagement and support.
• Training and Simulation: AI-driven realistic simulations are invaluable for training staff and volunteers, especially in high-risk or emergency scenarios.
• Environmental and Wildlife Conservation: Predictive modeling for environmental changes and wildlife tracking aids in strategizing conservation efforts.
• Legal and Policy Analysis: NGOs are using AI for analyzing legal documents and policies, aiding in advocacy and policymaking.

Navigating the Limitations

However, the integration of generative AI is not without its challenges:

• Bias and Ethical Issues: AI can perpetuate existing biases, raising ethical concerns in sensitive areas like social justice.
• Data Privacy and Security: Managing sensitive data, especially in conflict zones, demands stringent privacy and security measures.
• Data Quality Dependence: The effectiveness of AI is directly tied to the quality and quantity of input data.
• Contextual Understanding: AI’s lack of deep cultural, social, and political understanding can be a drawback.
• Resource Intensity: High computational resources for advanced AI models may strain smaller NGOs.
• Regulatory Compliance: Navigating complex regulations, especially in international operations, is challenging.
• Maintenance and Upkeep: Continuous system maintenance requires additional resources.
• Human Oversight: Ensuring accuracy and ethical compliance necessitates human intervention.

Limitations in Language Processing

One notable limitation of generative AI in NGOs is the challenge with languages, particularly underresourced ones. AI models, including language processing tools, are often trained on widely spoken languages, leading to a disparity in performance for lesser-known or regional languages. This can result in less accurate translations, content generation, and data analysis for NGOs working in linguistically diverse regions. Addressing this requires specific focus on developing and training models with datasets inclusive of these underresourced languages, which might be limited or hard to obtain.

How to Get Started with Generative AI 

Here is a process on how NGOs can adopt Generative AI in their organization in 2024. 

• Introduction to Generative AI: Understand the basics of Generative AI and its relevance to NGO operations.
• Identifying Opportunities: Analyze your NGO’s needs and identify how AI can address specific challenges.
• Building the Right Team: Gather a team with a mix of technical and domain expertise.
• Data Collection and Management: Learn about data collection, quality, and ethical considerations.
• Choosing the Right Tools: Explore various AI tools and platforms suitable for NGOs.
• Developing AI Solutions: Step-by-step guide to developing and training AI models.
• Integration and Testing: Tips on integrating AI into existing systems and processes.
• Training and Capacity Building: Educate your staff on AI usage.
• Ethical and Legal Compliance: Understand the ethical implications and legal requirements of using AI.
• Evaluation and Adaptation: Continuously evaluate the impact and adapt strategies as needed.
• Case Studies and Resources: Learn from real-world examples and access useful resources.

Omdena Case Studies: Generative AI Meets NGOs 

Here are four examples of Generative AI for Good projects. 

1. LLMs for Policy Assessment to Enhance Multilateral Negotiations

The project “Leveraging LLMs in Enhancing Multilateral Negotiations” with the German Federal Foreign Office, hosted by Omdena, focuses on using Large Language Models (LLMs) and Natural Language Processing (NLP) to improve decision-making in foreign policy negotiations. It addresses the challenges of accessing, processing, and leveraging vast policy-related documents. The project’s goals include data collection of various policies, data processing, integrating the Retrieval Augmented Generation (RAG) model, and developing a user-friendly interface for foreign policy experts. This initiative aims to provide an AI-powered assistant to streamline the negotiation process and generate actionable insights.

For more details, you can visit the project page here.

2. AI-Enabled Carbon Project Set-Up to Fasten Project Design

The project “Developing a Carbon Project Management Platform Leveraging Generative AI and ChatGPT” aims to simplify the creation of carbon projects. It involves developing a platform that uses GPT-4 and other AI models to assist users in designing carbon projects like reforestation and soil carbon sequestration. The platform will feature tools for feasibility analysis, project description creation, and generating complete project design documents. This initiative seeks to make carbon project development more accessible, thereby contributing to global carbon reduction efforts.

For more information, you can visit the project page here.

3. AI-Driven Content Assessment to Improve E-Learning

The project “AI-Driven Assessment and Feedback for Global Education Empowerment” aims to develop a Natural Language Processing (NLP) solution for accurately assessing essays and compositions. The goal is to provide personalized feedback using Generative AI, thereby improving e-Learning Content Creation’s efficiency and accuracy. The project leverages the partner’s dataset and existing technologies to scale up and enhance user engagement and community growth. It addresses the limitations of current systems and seeks to streamline the educational feedback process.

For more information, you can visit the project page here.

4. Improving Posture Screening to Enable Faster Patients Care

The project “Improving Posture Screening Using Generative AI and Reinforcement Learning” is a collaboration between iPlena and Omdena. It aims to develop a system for posture screening and correction using Generative AI and Reinforcement Learning. The project intends to make posture screening quick, easy, and accessible, utilizing photogrammetry, an optimized augmented reality model, continuous learning, and a posture prediction system. This initiative addresses the increasing issue of poor posture habits and related health problems.

For more details, you can visit the project page here.

The Omdena Edge: Empowering NGOs with AI Expertise

Recognizing these challenges and opportunities, Omdena is at the forefront, offering specialized Generative AI workshops and courses. These programs are designed to empower NGOs in identifying and harnessing the potential of AI in their unique domains.

We invite NGOs to collaborate with Omdena, explore our offerings, and embark on a journey to transform their impact through the power of Generative AI. Together, we can navigate the complexities and unfold the immense possibilities AI brings to the table in 2024.