#3/6. The Moravec Paradox: What Machines Do Well (and What Only Humans Can Do)
Understanding AI’s strengths and its profound limitations is critical to using AI well health care.
Why is it easier for AI to diagnose rare skin cancers than to reliably understand a toddler’s speech? Why can ChatGPT summarize my complex policy documents in seconds but struggle to navigate a conversation with a distressed patient in a noisy clinic? Welcome to the Moravec Paradox a concept at the heart of understanding the real limits and opportunities of AI in global health.
What Is the Moravec Paradox?
First articulated in the 1980s by roboticist Hans Moravec, (along with researchers like Marvin Minsky and Rodney Brooks), the Moravec Paradox is the observation that tasks which are easy for humans, like perception, movement, and social interaction, are extremely difficult for machines, while tasks that are hard for humans, like advanced math, data analysis, or playing chess, are often relatively easy for machines.
Moravec concluded that this counterintuitive insight stems from evolution: our brains have spent millions of years honing perceptual and motor skills, while formal logic and symbolic reasoning are comparatively recent additions. As a result, the most effortless human capabilities (like reading emotions, walking on uneven ground, understanding sarcasm) are the hardest for AI to replicate.
Why It Matters for Global Health?
In global health, the Moravec Paradox has real implications. Many high-burden settings require contextual reasoning, empathy, improvisation, and adaptability. These are things that are intuitive for trained humans but remain elusive for AI. Consider:
1. A frontline health worker calming a patient in distress while administering treatment in a conflict zone or refugee camp.
2. A community health volunteer navigating family dynamics to encourage HIV testing, in a rural village.
3. A nurse interpreting body language when language is a barrier and medical records are nonexistent.
These are easy for humans, and hard for AI. Yet, AI excels at other tasks. It can:
• Detect patterns across millions of patient records to forecast TB outbreaks.
• Optimize drug supply chains using predictive analytics.
• Translate and analyze data across languages and platforms faster than any human.
If global health programs assume AI will replace all human care, they may be disappointed. But if they focus on where AI can complement human strengths, eg, scaling data-driven decision-making, enhancing diagnostics, and automating repetitive tasks, they can unlock enormous value. Dont get me wrong, LLMs can be trained to be compassionate and may be excellent at deliverying stigma free care (colleagues from Audere demonstrate that with their Aimee tool at IAS this week!). But to navigate the Moravec Paradox, global health programs should do three things:
1. Focus on augmentation, not automation. In my opinion, AI should support and not replace health workers, especially in frontline roles where human judgment, empathy, and trust are irreplaceable. The WHO has great guidance on this, stressing how and where AI can augement health care work.
2. Invest in context-aware systems. AI tools must be designed with input from those who understand the lived realities of care delivery in LMICs. That includes accounting for language diversity, cultural nuance, and infrastructure variability. (The great GH champion Paul Farmer talked about this alot. Check out this paper that we wrote highlighting this, and these digital principles that underscore the importance of user-centered design!)
3. Build feedback loops. No AI tool is perfect at deployment. Programs should invest in real-time monitoring, participatory evaluation, and local adaptation to ensure that AI tools remain useful—and accountable. This demands a deliberative implementation science agenda that complements deployment.
Bottom Line: The Moravec Paradox reminds us that the hard part for AI isn’t crunching numbers but being human.
BTW if you are at IAS, check out this session on HIV and AI today. Some of whats here will be discussed there! Also more about the Lancet GH Commission on HIV and AI can be found here.