What is the state of the AI for Good ecosystem? This was a question proposed by a panel at UNGA with Payal Dalal, Dana Ima Hamzah,Uyi (O.T.) Stewart , Fred Swaniker, Komal Sahu, and I have been thinking a lot about it ever since. ⬆️ Hope: The potential of AI to drive meaningful change is immense. We’ve seen real, tangible solutions emerge—AI systems that support climate resilience, improve healthcare outcomes, and streamline social services. These aren’t just concepts; they’re proven examples of what’s possible when AI is applied thoughtfully. ➡️ Challenge: Yet, in many critical areas, the technology still falls short. While we’ve made progress, the reality is that most AI tools are not yet robust enough to deliver high-quality results in complex, domain-specific applications. Isolated success stories are promising, but they’re not enough. The challenge now is taking these breakthroughs and replicating them across the broader ecosystem to ensure that AI’s impact is felt where it’s needed most. ⬆️ Hope: The cost of building AI solutions has dropped dramatically—by around 1,000x in the last decade. And it will continue to drop. ➡️ Challenge: Access to high-quality, well-documented datasets. Currently, 85% of the organizations that can afford all data they need are defense & intelligence agencies. We need to invest in data interoperability and make these datasets available to those working on the frontlines of social impact. One good example is ODE Partners, which is working on making Earth observation models more accessible. AI should not be a hammer in search of nails. Too many organizations treat it that way, jumping on the bandwagon without a clear understanding of what problem they’re trying to solve. The focus should always start with the problem, and only then should we decide if AI is the right tool. 🔜 Winning strategy? Ecosystems! Success in AI for Good will not happen in a vacuum. We need to build ecosystems around every major impact vertical. Every accelerator or AI for Good program should consider having an “Ecosystem Coordinator-in-Chief.” We need people dedicated to connecting the dots, both within our organizations and across the sector, if we want to turn isolated wins into systemic change. If we want to move from theory to impact, we need the right tools, the right data, and most importantly—the right strategy. #AIEcosystem #SustainableDesign #AISolutions
Nora Marketos
2mo
I love your concrete recommendation on having in each program an ecosystem coordinator in Chief! I think for this to happen we need more professionals who can link the social impact and the AI tech world.
Chief Mentor, ZMQ (Ashoka Fellow)
2moUndoubtedly, there is indeed a huge promise of AI for Good (as we call it AI4Dev) but we need to be cautious of some challenges which may come our way –two of them being data availability and data justice. We know that AI systems rely heavily on large amounts of data for learning and prediction. In the context of AI for Good in LMICs, data arrangement is often weak or non-existent, which may be attributed to either lack of digitization or fragmented data or bias in data. A lot of AI for Good models are trained on data from rich and developed countries, which may not reflect the realities of our world. Also, we need to take care of fairness in data and quality in collection. In many cases, marginalized communities, for whom we are developing system could be exposed to data collection without meaningful consent and not letting them know the objective of program which eventually perpetuate inequalities.