We had the honor of participating in a special event last week on the theme of digital transformation for non-profits. Joining us at NetHope’s Global Summit in Dublin were speakers from Microsoft, Facebook and AWS.
NetHope is an organization that links the world’s largest nonprofits to its most important technology innovators. It was founded in 2001 and works with over 50 global NGOs in 180 countries to improve connectivity, access to IT services and to leverage data for impact. Our CEO Radha Basu was invited to talk about what digital transformation looks like in practice, sharing learnings from our work labeling and enriching the training datasets that power some of the world’s most advanced algorithms.
Radha started by reminding the audience that AI is powered not by code, but by models built from training datasets, which are themselves constructed by humans. Therefore, the quality and effectiveness of an AI tool is hugely dependent on the quality of the human nuance in the training sets.
This is an important insight when thinking about the future of the workforce in an AI-driven world, a theme with broad implications for the social sector. We see it everyday with our clients: the ‘AI Workforce’ is key to accelerating the digital transformation of businesses. Humans alone can accomplish the human-judgment tasks that power the next generation of computing services. To stay one step ahead of machines, the workforce of the future will have to be diverse, agile and scalable. From that point of view, AI is not a threat for future generations of workers, but rather an opportunity to create more jobs and digital inclusion.
The second part of the talk was dedicated to sharing examples of organisations using AI ‘for good’ in fields like the environment, medicine or to prevent crime. The MAAP project (Monitoring of the Adean Amazon Project) uses high-resolution satellite imagery to detect illegal deforestation in near real-time. Computer Vision is a technology that teaches computers to ‘see’ by feeding them large volumes of images annotated by humans. This technology is used to teach ‘cars’ to see, but can also be put to work to increase the precision and speed of satellite imagery analysis, and therefore improve the effectiveness of preservation efforts.
In the field of medicine, computer vision is already used to automate the detection of cancer cells. This is an extremely cost-effective way of democratising access to advanced diagnostics, for example in lower-income areas.
For non-profits looking to harness the power of AI to scale their impact, Radha charted the following ‘roadmap’: start first by identifying some of the problems with you can solve with AI, then look at your existing data sets. How can you derive insights and action points from their digitisation and labelling? If needed, accelerate data acquisition from the field. When manually labelling or enriching data, look for a large, diverse workforce: this will ensure that your data is more relevant by bringing in different cultural points of view. Finally, partner with universities or researchers looking for use cases at scale: they can help you define and grow your AI workflow with expert guidance.