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?

There must be a third way.

Within the social sector we have an opportunity to design for what we want—but to get there we’ll need to realign as a sector on how we use and, most importantly, fund this technology in line with the public interest. Building on research done at data.org, I am working with the Ford Foundation, social-sector community leaders, ethics experts, and foundation program officers to design a guidebook and set of workshops that help funders identify and support data and AI that is high impact while centering human flourishing. The hope is that this framework will begin to illuminate a third path for funders who may feel torn between capitalizing on the benefits of technology and risking their constituents in the process.

While research is still ongoing, this article shares a preview of early results from our work. Specifically, here are five ways that funders can think about investing in data and AI to put ownership of responsible data and AI squarely in the hands of the social sector.

  1. Laying the Groundwork: Read Up on Data and AI Ethics
  2. We’re All in This Together: Eliminate Factions
  3. Thinking Machines: Talk About Human and Computer Outcomes Together
  4. Good Data Design Is a Process
  5. A New Profession: Supporting Talent

Read the full article about data and AI in the social sector by Jake Porway at Stanford Social Innovation Review.