Enterprise AI Analysis
Bridging AI and Humanitarianism: An HCI-Informed Framework for Responsible AI Adoption
Advances in artificial intelligence (AI) hold transformative potential for humanitarian practice. Yet aligning this potential with the demands of humanitarian practice in dynamic and often resource-austere contexts remains a challenge. While research on Responsible AI provides high-level guidance, humanitarian practice demands nuanced approaches for which human-computer interaction (HCI) can provide a strong foundation. However, existing literature lacks a comprehensive examination of how HCI principles can inform responsible AI adoption in humanitarian practice. To address this gap, we conducted a reflexive thematic analysis of 34 interviews with AI technology experts, humanitarian practitioners, and humanitarian policy developers. Our contributions are twofold. First, we empirically identify three cross-cutting themes—AI risks in humanitarian practice, organisational readiness, and collaboration—that highlight common tensions in adopting AI for humanitarian practice. Second, by analysing their interconnectivities, we reveal intertwined obstacles and propose a conceptual HCI-informed framework.
Key Findings at a Glance
Our analysis reveals critical insights into AI adoption in humanitarian contexts, combining expert perspectives with quantitative observations.
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Interviews Conducted
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Avg. Practitioner Experience
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Women Less Likely to Own Mobile Phones Globally
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI Risks in Humanitarian Work
Organisational Readiness
Collaboration for AI Deployment
AI Risks in Humanitarian Work
Data Privacy and Ownership: Humanitarian organizations manage large volumes of sensitive data in volatile environments. Misuse can endanger lives, undermine trust, and exacerbate inequalities. Robust data governance, beyond mere protection, is crucial. Bias and Representation: AI systems rely on data quality. Biased or incomplete data (e.g., gender gap in mobile phone ownership) leads to inaccurate outcomes, perpetuating inequalities. Standardized data management is essential. Ethical Challenges: Dual-use scenarios (AI for military/political gain) raise profound ethical concerns. Premature adoption without safeguards is cautioned. Incremental approaches and robust ethical education are vital. Data Transparency and Accessibility: Tension between openness for common good and protecting sensitive information. Context-sensitive governance frameworks are needed to balance transparency with safeguarding vulnerable populations.
Organisational Readiness
Gaps in Literacy: A widespread lack of understanding of AI's implications among humanitarian staff. Critical dimensions include competency, capacity, capability, and organizational culture. Training and education programs, accounting for diverse crisis contexts and resource scarcity, are urgently needed. Basic infrastructure limitations further magnify these challenges. Leadership: Leaders play a pivotal role in advocating for appropriate AI adoption, securing resources, and aligning workforce with strategic goals. They must navigate centralization vs. localization, upholding humanitarian principles. Governance: Robust frameworks are needed for ethical and effective AI use, focusing on accountability, inclusivity, and actionable implementation. This includes early standard action protocols, audit trails, and explainability. Current regulatory landscape is fragmented, requiring translation of high-level policies into operational resources. Sustainability of AI Projects: Challenges include long development cycles (6-9 months) that can be overtaken by events, high technical costs (GPUs), and limited funding focused on immediate needs over long-term capacity building. Dependency on proprietary systems is unsustainable.
Collaboration for AI Development and Deployment
Cross-Sector Collaboration: Academic institutions, public sector agencies, and other humanitarian organizations offer critical research, contextual understanding, and resource pooling. However, concerns exist about academia approaching problems in isolation, overlooking humanitarian principles. Effective collaborations ensure AI tools are contextually relevant and technically robust. Many UN agencies subcontract AI development, highlighting the need for agencies to own the problem and oversee technical support. Local engagement, co-creation, and inclusive governance are crucial to ensure AI systems reflect specific needs and are grounded in lived realities, not external assumptions. Ethical Considerations in Partnerships: Tension arises when external partners (e.g., tech companies) prioritize technical performance (accuracy) over humanitarian goals (dignity, ethical imperatives). Partnerships should focus on practical value for end-users, not just technical innovation. Risks of 'participatory washing' (superficial inclusion) are noted, undermining genuine ethical participation. Power dynamics (Global North-South divide) necessitate equal partnerships valuing local expertise and agency.
200M
Fewer Women Globally Own Mobile Phones (Compared to Men, amplifying bias risk)
UNHCR Rohingya Biometric Data Incident (2018)
UNHCR collected biometric data from Rohingya refugees for aid distribution. The subsequent sharing of this data with Myanmar authorities raised serious safety concerns, exposing gaps in informed consent processes and data protection. This incident underscores the urgent need for robust ethical guidelines and safeguards to prevent actions that cause harm.
Quote: "This case underscores the urgent need for robust ethical guidelines and safeguards to prevent actions that cause harm." (H15)
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ECHO Framework for Responsible AI Adoption
Educate: Foundational AI Understanding
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Co-Create: Collaborative Solution Shaping
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Handhold: Skill Transfer & Local Ownership
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Optimise: Iterative Refinement & Adaptation
Calculate Your Potential AI ROI
Estimate the time and cost savings your organization could achieve with a responsible, HCI-informed AI strategy.
Estimated Annual Savings
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Hours Reclaimed Annually
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Your HCI-Informed AI Roadmap: The ECHO Framework
Implement a structured approach for responsible AI adoption, building ethical capacity and fostering equitable collaborations.
Educate: Foundational AI Understanding
Humanitarian staff gain foundational understanding of AI's ethical implications, data risks, and potential biases through scenario-based tutorials and intuitive interfaces, identifying red flags like sensitive data over-collection or dual-use potential.
Co-Create: Collaborative Solution Shaping
Affected people, practitioners, policy experts, and technologists collaboratively shape AI solutions, ensuring they reflect contextual realities, local needs, and humanitarian values, countering tokenistic engagement.
Handhold: Skill Transfer & Local Ownership
Transfer skills, decision-making authority, and system adaptability to local organizations through incremental AI features and modular learning supports, reducing long-term dependence on external actors and fostering ongoing capacity strengthening.
Optimise: Iterative Refinement & Adaptation
Institutionalize iterative refinement and continuous feedback loops so AI systems remain ethically sound, accurate, and aligned with humanitarian principles over time, adapting to shifting crisis conditions, data challenges, and political landscapes.
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