DataKind has teamed up with Microsoft to use data for good yet again, and we couldn’t be more thrilled!
At DataKind, our mission is to harness the power of data science and AI in the service of humanity. Similarly, Microsoft believes that AI, when designed and used responsibly, can help tackle big societal challenges and bring benefits to humanity. However, for too many organizations, the positive impacts of using AI remain out of reach. To help these organizations build the capacity to take full advantage of the many benefits of AI, DataKind and Microsoft have designed an “AI Accelerator” program, in which we're providing pro bono expertise and assistance to a cohort of organizations seeking to apply AI to societal challenges in urban communities.
In the Accelerator, we lead the organizations through a close collaboration with volunteer AI and data science experts to map out their pain points, identify ways their data could be used to support AI solutions to meet their missions, and scope specific applications for their projects. The work will culminate in a virtual DataDive on Sunday, October 6, 2019. This is a high-energy marathon-style event where broad teams of socially conscious quantitative experts will dive into prototyping to craft and validate the various organizations’ AI projects.
Ultimately, the participants in the AI Accelerator will have the opportunity to learn, build advanced technical capacity, and execute on AI projects that might otherwise remain out of reach, all to the benefit of the communities they serve. They will finish with actionable project plans for taking their AI journeys forward. More broadly, institutions that aim to enable application of AI for societal benefit, such as DataKind and Microsoft, will benefit from the cross-cohort learnings and models for how to expand the use and impact of AI by community-focused organizations.
Many organizations applied with great projects, and several community leaders and data for good experts provided thoughtful input, nominations, and outreach. In the end, we narrowed it down to a select cohort of organizations as listed below. Please join us in thanking our nominators and congratulating our cohort!
Do you want to learn more about the organizations involved? Are you interested in volunteering? Have you checked out our other projects with Microsoft? Read on for more information.
We’ve worked with Microsoft on a number of cross-sector data science and machine learning scoping and execution engagements. From using data science to improve traffic safety across New York, Seattle, and New Orleans to bringing data science and education expertise together to advance student success, our collaboration with Microsoft has shown the power of scoping projects in cohort and applying data science and machine learning capacity to organizations’ work. We’ve used our joint learnings from those engagements to inform this project.
Microsoft brings data and technology to bear on society’s most pressing challenges facing communities, and we’re looking forward to continuing to work together to drive responsible and impactful uses of technology and AI for social good!
Introducing the AI Accelerator Cohort
The participants include nonprofits addressing community challenges, city government departments, and mission-driven startups in cities across the US, working in important areas such as sustainability, housing, education and workforce, and city services.
For a list of some of the organizations involved, see below.
- Bloc: Bloc is building a platform that helps job placement agencies improve their clients’ job application and interviewing skills. In this project, Bloc will introduce natural language processing and text analysis to deliver targeted suggestions about how resumes and cover letters could be improved to match the needs of particular job descriptions.
- Boston University: The team at Boston University is embarking on a project to integrate machine learning and predictive modeling into regional sustainability and net-zero planning. This will enable more accurate forecasts and better decision-making about how to eliminate carbon dioxide and greenhouse gas emissions from the regional economy.
- Code for Miami: The Miami metropolitan area faces a significant housing affordability and accessibility crisis, and Code for Miami is creating a new portal to enable city administrators and residents to better identify available units, as well as to predict and identify cases where landlords and property managers may be engaging in discriminatory or predatory rental practices.
- Connecticut Coalition to End Homelessness (CCEH): CCEH will build on their track record of applying data analysis to the understanding and reduction of homelessness by integrating new data from the state Department of Corrections. The outcome of this project will be used to inform policy in the coming legislative session. The focus is to reduce the cycle between incarceration and homelessness and to better target resources to help residents find housing and supportive services.
- City of Racine, Wisconsin: Racine, the smallest municipality to receive the Smart City designation, has implemented data collection and analysis systems that allow city officials to examine the activity of each city service in isolation. In this program, the city will use AI pattern recognition methods to explore the spatial, temporal, and causal relations between city services, so that resources can be prepositioned and allocated more effectively with some problems being preempted altogether.
- St. Louis Regional Data Alliance: Following decades of population decline, the City of St. Louis faces very high vacancy rates, and these unmaintained properties suppress the value of nearby residences. In this program, the St. Louis Regional Data Alliance is partnering with the City to implement predictive models that quantify the potential increase in property values and tax base that would result from various remediation efforts.
- Teach for America: As a key part of supporting its nationwide program, Teach For America runs multiple surveys for its teachers, known as corps members each year. For this program, the team will apply natural language processing and text analysis methods to the survey responses to better understand factors that predict participant success and retention.
- Trust for Public Land (TPL): The Trust for Public Land has the goal of cataloguing the amenities that are available at every city park in the country, so that citizens can better enjoy the parks and city officials can more efficiently and equitably target their maintenance and improvement activities. The Trust for Public Land will employ deep learning and computer vision methods to automatically identify park amenities from satellite and street-level imagery.
Volunteering for the Virtual DataDive
Flex your data skills and apply to join us to help accelerate these projects! If you’re interested in volunteering for the virtual DataDive on Sunday, October 6, 2019, please complete a volunteer application by Monday, September 16, 2019. Space is limited, so we’ll let you know if you've been selected. Thanks for applying to use your skills on our data for good projects, and be sure to follow us on Twitter, LinkedIn, and Facebook where we regularly announce volunteer opportunities that come up.
Questions? Please contact us at firstname.lastname@example.org.