Our Program

DataRobot AI for Good is a free program that provides nonprofits with access to our software and up to six months of hands-on help from our customer success teams. It is designed to address the shortcomings we found with other data for good initiatives by making sure participants have access to the specialized help they need to create high-quality, lasting AI solutions.

The program takes into account project requirements and organizational needs. Some participants didn’t have many engineers on staff, so they received extensive hands-on help to develop their AI solution. Other participants had a team of engineers. They consulted with our data scientists and used the advanced capabilities available through our API to develop their AI solution. 

Our team provides the right resources for each project: our data scientists provide expertise on the building and deployment of AI models while our AI success managers help integrate the solution into the nonprofit organization. Our Solution engineers can help design and integrate end-to-end solutions and our UX team can help bring the front-end to life, if needed.

The First Round

During our first enrollment period, we received dozens of applications from ten countries and selected five participants: Kiva, DonorsChoose, Anacostia Riverkeeper, UCSF Brain and Spinal Injury Center, and Mannheim University Hospital. 

Their projects covered topics as diverse as studying the drivers of patient outcomes in the operating room to predicting which microloans were unlikely to be funded. 

Here are some program highlights:

• Kiva trained and deployed dozens of models to develop a system to promote loans that aren’t being viewed and are at risk of going unfunded. The organization now promotes high-risk loans on their website to increase their visibility and improve the chances of funding. 
• Using operating room data, UCSF used DataRobot to study the drivers of outcomes for patients with traumatic spinal cord injuries. They discovered a previously unknown relationship between blood pressure and patient outcomes, which will be studied further to improve patient care. Importantly, the time arrive at a powerful insight was reduced from the typical 2-3 years to 6 months.

In addition to the hands-on help, each participant received an additional year of access to the DataRobot platform. 

What We Look for in Participants

In our search for AI for Good participants, we look for projects that have impact, framing, and readiness. 

Above all, we want to make sure that projects have an impact in the communities served by our program participants. It is also important that the problem can be solved by AI. There should be a specific event that is being predicted or studied, whether that be microloan funding, unsafe levels of E. coli in the water, or the impact of many variables on patient outcomes. We can help flesh this out, but our ideal applicants have a vision for how they can leverage their data to solve  an important problem. Lastly, because delivering an AI solution can be difficult, we want to make sure that the leadership and stakeholders in nonprofit organizations are committed and prepared to implement their project.

The Next Round 

The application period for the AI for Good program runs from March 1-April 30,  after which we will start scheduling interviews. We expect to contact potential participants in June. 

You can apply here for the AI for Good program. 

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AI for Good: Powered by DataRobot traces its inspiration to Chandler McCann’s work with the Global Water Challenge in 2018. Following the success of the project, which expanded from Sierra Leone into Liberia, Chandler and our CEO Jeremy Achin wanted to find a way to expand the work within DataRobot too. Chandler assembled a team to formalize the program, spearheading the creation of AI for Good: Powered by DataRobot. GlobalGiving partnered with us to facilitate the design of the program and the vetting of applicants, with their extensive history and expertise in working with networks of NGOs and nonprofits.

Together, the GlobalGiving and AI for Good teams did their research, investigating other “data for good” efforts to identify unanswered needs and how best to play to DataRobot’s unique strengths as a program. At DataRobot, we prioritize the democratization of data science. Through the power of automation and intelligent guardrails, the DataRobot AI platform empowers the citizen data scientist–an individual who is not a data scientist, statistician, or programmer by trade–to build predictive models. In addition to the platform, we have a deep bench of data scientists, engineers, and change management experts experienced in successfully designing and deploying enterprise AI solutions. Through pro bono software licenses and the project support of our success teams, the AI for Good program aims to bridge the gap between the technologies and data scientists of machine learning and the champions of social and humanitarian causes.

Voices from the AI for Good Team

For Libby Botsford, DataRobot communications manager and a founding member of the team, the chance to be a part of this program has been incredibly meaningful: “I’m proud to work for an organization that not only wants to make a difference in the world but that puts its money where its mouth is. Our AI for Good program is truly one-of-a kind—we’re combining the world’s smartest data science talent with the most powerful AI platform to build long-lasting solutions to some of the world’s toughest challenges.”

After several months of research, planning and hiring, the rest of our team was fully assembled. Belén Sanchez, a data scientist hired to be dedicated to the AI for Good program, brought years of expertise in the social and humanitarian space to the table: “I joined DataRobot after working for 12 years promoting inclusive social and economic change with governments and international organizations around the globe. I believe this is a unique opportunity to accelerate the discovery and deployment of AI solutions to urgent social, environmental and economic problems.”

Natalie Bucklin found a way to marry her expertise in data science with her passion for nonprofits, having served on the board of directors for a nonprofit in DC and volunteered with Compass, a pro-bono consulting group, for a number of years.

Paul Fornia, another of our dedicated data scientists, explained why he was drawn to this program simply: “Who wouldn’t want to spend their day getting to solve important social problems, while also getting to work at the forefront of AI and Machine Learning?”

The program publicly launched in July. We began accepting applications immediately with an open request for proposals on our site (apply here). As the first round came in, our team met with GlobalGiving at their office and with Brian Banks (Director of Strategic Initiatives at the Global Water Challenge) to carefully evaluate applications and select awardees. Our criteria were scalability, sustainability, impact, and machine learning applicability—which more than fully describes the strong use cases presented by our first program participants. Now most of these projects are underway, in the early phases of data acquisition and modeling. Let’s take a moment for a few words on each.

Meet the Participating Organizations

Anacostia Riverkeeper is a non-profit dedicated to protecting the Anacostia River and other waterways in the DC region that recently launched a water quality program for measuring E. coli samples at sites throughout the DC area. DataRobot and Anacostia Riverkeeper will build a forecast of E. coli levels for the Anacostia River to be able to predict the state of the river on a given day, and potentially extend the methodology to other Riverkeeper organizations and their waterways around the country and the world. Paul is leading our data science team for Anacostia Riverkeeper, and said, “As someone who often enjoys kayaking and hiking in and around DC’s rivers, I have a lot of respect for the efforts of this team. If our efforts can be expanded to other urban Riverkeeper organizations in the US, or even around the world, the impact could be truly massive.”

DonorsChoose makes it easy for anyone to help a classroom in need, moving us closer to a nation where students in every community have the tools and experiences they need for a great education. They will use DataRobot to predict teacher and donor churn within the platform.  Due to the scale of the platform, increasing donor and teacher retention by a few percentages could lead to hundreds of thousands in additional funding distributed to more schools and reaching tens of thousands of more students in a given year. Paul said, “It’s great working with DonorsChoose because of the scale and breadth of their impact and the massive teacher network they reach every day. Even modest improvements will translate into countless more funded projects, bringing students across the country everything from basic supplies like books and snacks, to art supplies for special needs classrooms, to reading programs for ESL students.”

Kiva is a tech non-profit organization that has built a crowdfunding platform enabling lenders to provide microloans to about two million low-income entrepreneurs in about 80 countries; it will use DataRobot to predict and promote loans that have a high likelihood of not funding fully and therefore potentially expiring. Decreasing expiring or unfunded loans will lead to tens of thousands more loans being distributed to low-income entrepreneurs. Microloans have proven to be an incredible solution to a hard problem in the developing world, where low-income entrepreneurs are unable to meet the type of credit history or steady income requirements needed to secure a traditional loan to support a small business. Speaking personally, as one of the data scientists working with their fantastic team, I’m just as motivated by the loans with a more modest purpose—some examples from Kiva: to install a pump for clean water to a family, to pay off an unexpected hospital bill, to buy better fertilizer—that research has shown make a direct impact improving quality of life.

Medical Faculty Mannheim – Heidelberg University is a university hospital that will use DataRobot to better understand the factors and predictors of the World Health Organization’s top causes of death using a large multi-year dataset covering tens of millions of hospital visits. Better predictions of mortality could inform doctors, hospitals, or even government agencies. Boris Cordes, the DataRobot account executive overseeing the relationship, said, “They came up with the great idea of leveraging DataRobot to help patients around the globe to survive severe diseases, get better soon and enjoy a healthy and long-lasting life. I can’t wait to see first results and the positive impact that these predictions will have on a more intelligent and healthy tomorrow.”

University of California San Francisco (UCSF)’s Brain and Spinal Injury Center (BASIC) is one of the leading research institutions on spinal cord injuries in the U.S.; care providers associated with the institution will use DataRobot to predict patient outcomes in critical situations, allowing for more precise decision-making, building on the hospital’s leadership in traumatic brain and spinal injuries. This project also has the potential to refine guidelines for treating traumatic spinal injuries, impacting thousands of patients across the US. Josiah Tubbs, an AI Success Manager at DataRobot volunteering to help run this project, sees his participation this way: “Our purpose in life is to find our gift and give it away to others freely. Our CEO, Jeremy Achin, believes strongly in sharing the gifts we have here. That’s why I volunteered to work with UCSF and assist their team to drive research in the area of spinal cord injuries. If we can help even one person recover faster or more fully from an spinal cord injury, by enabling the UCSF research team, then we’ve accomplished our mission.”

Our team will continue to provide updates on their progress. Looking to the future, Natalie said, “Our AI for Good program has existed for less than a year and we’ve already had applications from over 10 different countries. This demonstrates the demand in the non-profit space for help with solving AI problems.” AI for Good: Powered by DataRobot is now rolling admissions, and we will continue to seek new organizations to partner with and pursue machine learning use cases that make the world a better place. Colleen Wilhide, supporting the program from the office from the CEO, summarized, “It’s no secret that the opportunities for AI to impact our world are limitless. The ability to amplify and scale the extremely important work of nonprofits around the globe has been paramount to us here at DataRobot, and we’re only just getting started.”

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In addition to health problems, the lack of access to drinkable water has huge negative impacts on other aspects of life. Without functioning water points, people are forced to walk long distances, over 30 minutes to an hour, to wait in line for their daily water supply and then carry it all the way back to their homes. This task usually falls to women, which has large impacts on gender equity and education, as this task can occupy a substantial part of their day. 

The Data-Driven Solution

DataRobot’s customer, the Global Water Challenge, wanted to understand why these breaks were occurring, so they began gathering data for the first time. Although there had been massive investments in water point construction, no one had a complete picture of water point functionality. Data was scattered across multiple sources, even within one country, and generally collected in different formats and mediums. 

Enter Brian Banks, Director of Strategic Initiatives at the Global Water Challenge. Brian wanted to harness the data that existed in a holistic way that was useful. But, what data is the right data to collect? Brian spent nearly two years traveling around the world asking experts this question: “How do we create a data standard for water points?”

Out of these conversations, the team built what is now known as The Water Point Data Exchange (WPDx), the first harmonized database of water points from around the world. WPDx allows countries and organizations to share their water data, resulting in a database that grew from tens of thousands of data points to over half a million today. 

Consolidating the data was a huge task, and once complete, begged the question: What do we do with it? Brian is not a data scientist but knew there were useful insights in the data that were beyond simple dashboarding. Brian tried all of the ‘data for good’ routes available to him: free consulting, cloud resources, and even working for months to set up a hackathon. Some results were interesting, but none really had the impact he was hoping for, and in all cases (since Brian couldn’t code), he couldn’t work with the code-based products they left him.

When Brian started working with DataRobot, things changed. In a few hours, Brian was able to upload his data from WPDx and build a model to answer some of the important questions he’d been looking for, such as, “Can we predict which water point will be broken in the future?” In an afternoon, he was able to accomplish on his own what other groups had attempted to do over the course of a year. 

Working with DataRobot, Brian built models for 13 countries and began integrating these predictions in to a web app that maps out which water points are working (or not working) along with meta-data around the type of water points, the water source, location, repair priority, and (crucially) which water points are likely to be broken in the future. 

What Comes Next?

The models Brian built with DataRobot are some of the first, if not the first, large scale uses of machine learning on water point data.  The initial response by governments has been overwhelmingly positive as these tools help focus resources in resource-constrained environments. Now we’re exploring other areas where DataRobot can help address issues with distributed infrastructure and how we can actively work with key stakeholders on the ground to improve the data coming into the tool. 

We are continuing to work with local governments on the ground to train users on the new tools, sharing how machine learning can help them in their daily lives, and collecting constant feedback to ensure the data is useful. We’ve seen an enormous response from the first six month pilot in Sierra Leone. Today, they are using the output of the platform to inform the planning process for repairs, maintenance, and new construction of water points, impacting nearly two million citizens across the country. And we are in the process of expanding this program to other countries as well!

We’re very excited about the work that addresses the important issue of access to water and are honored to be a part of this project with Brian and the Global Water Challenge. We have big dreams for how we can leverage automated machine learning to solve the world’s biggest challenges. 

About the Author:

Chandler McCann is a Senior Data Scientist at DataRobot, where he leads the federal data science practice, as well as the AI for Good: Powered by DataRobot program. Chandler has over 15 years of experience in analytics and data science. He received his Masters in Information and Data Science from UC Berkeley and his undergraduate in Materials Science Engineering from the University of Maryland. With GWC, Chandler has worked closely with the governments of Liberia and Sierra Leone to improve access to water and has a passion for leveraging AI for societies’ toughest challenges.

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