“Machine learning is not always the answer”
for social impact products and projects. This is the primary takeaway from Google’s 47-page report on their AI Impact Challenge, which awarded $25 million in funding to 20 grantees (see DT #13
and DT #15
). Google received 2,602 proposal from 119 countries and analyzed them for “a view of the AI for social good landscape,” which they present in the report. Assuming grant proposals are an unbiased estimator:
- The most common area for social good AI projects is health (27% of proposals), followed by environment, conservation, and energy (16%), and education (12%).
- The most common AI capability for such projects is computer vision (41%), followed by general “analytics” (26%) and deep learning (18%); noticeably, reinforcement learning, which is a big focus of Alphabet’s DeepMind research group, is used by very few real-world projects (2%).
- Academic institutions are most likely to already be using AI in some way (85%), where not-for-profit organizations are least likely (44%).
Digging deeper, Google noted that for many social impact projects, machine learning just isn’t applicable. Hopefully this report will help poke a hole in the “sprinkle some AI on top and call it a day” enterprise hype that has been growing in the past few years:
Some organizations submitted proposals that might be better implemented without machine learning by leveraging other methods that could result in faster, simpler, and cheaper execution. Other applicants underestimated the complexity of the work required, and in still other instances, machine learning is not yet sophisticated enough to make the proposed solution viable.
That definitely corroborates the notion that the current AI hype is giving a lot of people wrong ideas about the usefulness of machine learning in their domains. The report’s overall seven main insights are:
- Machine learning is not always the answer.
- Data accessibility challenges vary by sector.
- Demand for technical talent has expanded from specialized AI expertise to data and engineering expertise.
- Transforming AI insights into real-world social impact requires advance planning.
- Most projects require partnerships to access both technical ability and sector expertise.
- Many organizations are working on similar projects and could benefit from shared resources.
- Organizations want to prioritize responsibility but don’t know how.
I’m not going to dig into all of these here, so check out the full report for detailed descriptions and specific recommendations for funders, organizations using AI, and policymakers: