Funding Data and AI That Serve the Social Sector
Five smart ways to think about investing in data and AI
When I co-founded DataKind in 2011 to connect volunteer data scientists with nonprofits looking to increase their impact, I did so with a vision of data science and AI being used, first and foremost, in the service of humanity. In my eyes, the social sector had a fantastic opportunity to not just catch up to the for-profit uses of tech that were changing industries and disrupting the world, but to instead race ahead with its own vision of success.
More than 10 years later, there has been an influx of money dedicated to data for good and AI for good efforts, and more projects have demonstrated applications of data science in the social sector. However, the impact of these efforts does not yet square with the bullish visions of a Fourth Industrial Revolution that will unlock a new era of human flourishing. Worse, these positive cases don’t seem to counterbalance the horror stories of technology gone wrong that we are regularly treated to, opening our eyes to the ways algorithms reinforce systemic inequities, harm already underprivileged communities, and are used for crimes against humanity by nefarious actors.
There is, understandably, a growing tension in social-sector organizations that seek to take advantage of the benefits AI can provide for civil society while not exacerbating the systemic inequities that already permeate our communities. Are our only options to charge ahead with innovation, hoping for the best? Or to shun data science and AI in the social sector as more harm than good?
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