This project aims to develop a comprehensive governance framework to ensure that AI systems used in humanitarian contexts are ethical, transparent and accountable. By focusing on protecting the rights and dignity of vulnerable populations, this framework will guide the ethical deployment of AI tools in humanitarian aid ensuring that technology is used to enhance not undermine humanitarian efforts.
Establish ethical guidelines
Develop protocols to mitigate biases and ensure cultural sensitivity in AI systems.
Ensure informed consent and transparent communication with affected communities.
Promote transparency and accountability
Implement human-in-the-loop processes to maintain oversight in AI-driven decisions.
Define clear responsibility for AI outcomes and establish feedback loops for continuous improvement.
Align with international standards
Ensure compliance with international humanitarian laws and local legal contexts.
Promote the use of open-source AI tools for greater transparency and accountability.
Framework development
Collaborate with AI ethics experts, humanitarian organizations and local communities to co-create the governance framework.
Pilot the framework in select humanitarian contexts (refugee camps, disaster response) to refine and validate its effectiveness.
Capacity building
Develop and deliver training programs for humanitarian workers on the ethical use of AI.
Create toolkits and resources to help organizations implement the framework in their operations.
Monitoring and evaluation:
Establish continuous monitoring systems to track compliance and ethical performance of AI systems in the field.
Regularly evaluate and update the framework based on feedback and evolving best practices.
Research and development
Conduct thorough research to identify existing gaps in AI governance within the humanitarian sector.
Develop the framework, toolkits and training modules.
Pilot projects
Implement and test the framework in real-world humanitarian settings to gather data and refine the approach.
Capacity building
Design and deliver training programs and workshops for humanitarian workers and local communities.
Monitoring and evaluation:
Set up continuous monitoring systems and conduct regular evaluations of the framework’s impact.
Gideon Abako (Project Lead)
Extensive experience in humanitarian work including health, nutrition and supply chain projects in Nigeria and Uganda.
Strong background in integrating AI and technology solutions in international development projects.
Dr Mukumbuta Nawa Talama: Humanitarian Aid Specialist with experience in implementing technology in crisis situations.
Track record
Successfully led projects integrating AI solutions in humanitarian settings including developing nutritional economics tools and optimizing supply chains with AI.
Proven ability to secure grants and deliver impactful, technology driven humanitarian solutions.
Limited adoption by humanitarian organizations
Cause: Humanitarian organizations may struggle to adopt the framework due to resource constraints, lack of technical expertise or resistance to change.
Outcome: The framework may not be widely implemented limiting its impact on AI governance in the sector.
Insufficient funding
Cause: Inadequate financial support could hinder the development, piloting and scaling of the framework.
Outcome: The project may stall or be unable to reach its full potential reducing its effectiveness and scope.
Challenges in customization and scalability
Cause: Difficulty in adapting the framework to diverse cultural, legal and operational contexts across different humanitarian settings.
Outcome: The framework may become too rigid or complex leading to poor fit with on-the-ground realities and limited practical application.
Technological and ethical complexities
Cause: The complexity of balancing technological innovation with ethical considerations might lead to oversights or unaddressed ethical dilemmas.
Outcome: If key ethical issues are not effectively managed the framework could fail to protect vulnerable communities undermining its credibility.
Lack of engagement and buy-in from key stakeholders
Cause: Insufficient involvement of local communities, humanitarian workers and other key stakeholders in the development process.
Outcome: The framework may not fully reflect the needs and realities of those it is designed to protect leading to mistrust or rejection by users.
Seeking further funding to expand pilot projects, enhance training programs and scale the framework’s implementation globally.