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Humanizing artificial, expanding intelligence: Putting AI in context with the social sciences and the social sector

The relationships between artificial intelligence (AI), the social sciences, and the social sector have incredible potential. It is easy to imagine a disconnect between something that is, in name, artificial, and the study and support of human relationships. How are advancements in data science, such as AI, furthering the missions and shared objectives of non-profits, community-based organizations, and government agencies? While AI’s potential to mitigate bias and improve impact has many excited, some cautioned its potential to replicate or exacerbate harmful practices. Lynnea Brumbaugh of the McKelvey School of Engineering describes this dynamic: “[AI is] just a tool – as beautiful and brutal as our own minds.”

AI is just one of a range of techniques to increase impact with data. Given the speed and scale at which data science approaches can be implemented, it is critical for social sector organizations to understand the foundations of equitable data practice and strategy and building understanding of data across people in the organization. These are precisely the kinds of considerations and organizational opportunities that inform the Data for Social Impact initiative (DSI). Supported by the Mastercard Center for Inclusive Growth in partnership with the St. Louis Regional Data Alliance and, DSI is designed to build capacity and collaboration around equitable and community-centered data practices in the St. Louis social sector.

AI in the Social Sciences

To support building shared understanding and connectedness around these topics, DSI and the Social Policy Institute co-sponsored a recent Brown School Open Classroom series, AI in the Social Sciences. Across the five events, experts from a variety of fields explored the strategic deployment of AI to draw fuller conclusions from big data sets, while also attending to equity issues and mitigating potential harms. Topics included common misconceptions about AI, its economic applications, potential difficulties in using AI in social science fields, and the social and ethical implications of these tools. The series was organized by Ruopeng An, associate professor of the Brown School of Social Work, who was joined by cross-discipline speakers including Professors Xiang Hui, Dennis Zhang, and Liberty Vittert of the Olin Business School and Professor Lynnea Brumbaugh of the McKelvey School of Engineering.

Utilizing data and AI equitably

The event series emphasized that despite its great potential, AI is not a replacement for human judgment, nor is it neutral and free of bias. AI learns from humans and is subject to many of the same biases of the humans who build it. Like all data tools, AI must be approached with humility and a recognition that the data it generates can significantly impact people’s lives and their communities. As Dr. Liberty Vittert of the Olin Business School said, “it’s so easy to become data-driven and to forget about the human side—to forget about the fact that every number is a human person.”

Dr. An’s passion for AI is fueled by his dual commitment to the intersection of social change and rigorous science. “AI and the social sciences need each other,” An said. “The social sciences need AI to revolutionize their methodologies to observe, interpret and analyze phenomena better; AI needs social sciences to mitigate potential harms of real-world applications and to ignite positive social change.” In addition to the Open Classroom series, Dr. An teaches courses on the applications of AI in practice and developed a new certificate program available to Brown School graduate students. For professionals who share an interest in the opportunities and challenges of AI, the Brown School also offers a post-master’s certificate in artificial intelligence. The program will provide hands-on, step-by-step guidance and practice opportunities to learn and apply state-of-the-art AI models to solve real-world health and social problems. Applications for this program will close on August 10, 2022.

Outside of formal classroom settings, social sector organizations can take advantage of free opportunities such as the AI in the Social Sciences series as well as programming offered by the DSI Initiative, including an upcoming event, It’s a Process, Not a Product: Building Equitable Data Infrastructure, and a series of self-paced online learning modules launching this fall. Another helpful and free resource is’s introduction to AI guide, which gives insight into what AI is, how nonprofits can use it, and as a primer on getting started using machine learning. Given the increased influence of data science in decision-making and the potential for improving outcomes and impact, AI is a topic the Social Policy Institute and DSI initiative will increasingly explore in future offerings. Sign up to stay informed of future SPI DSI offerings here.

Resources and Events’s helpful “Introduction to Artificial Intelligence”

AI in the Social Sciences Events:

Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform

What Do People Mean When They Say AI, ML, Big Data, or Data Science?

The Beautiful, Brutal Binary: Why the Problems of Artificial Intelligence Can’t Be Solved by AI

Artificial Intelligence: Applications, Promises, Pitfalls, and Misperceptions

Artificial Intelligence: Implications for Social Equity and Bias.

DSI Initiative Events:

We Are All Data People: An Equity-Centered Approach to Increasing Impact

Start Where You Are: Mapping a Journey Toward Equitable Data Practice

Data-Informed, Equity Driven: Cultivating a Collaborative Data Culture

Sharing Data, Sharing Power: Tools and Tensions in Collective Data Efforts

How Can a Data-Informed Social Sector Amplify Impact in St. Louis? – May 2021
Read the Report & Watch Recording

Mapping Your Data Journey from Vision to Impact – Oct. 2021
Read the Recap & Watch Recording

Using Data to Promote Equitable Outcomes – Nov. 2021
View Event Slides & Watch Recording

Community-Centered Data Practice: Data Protection, Data Sharing, and Who Benefits? – Nov. 2021
View Event Slides & Watch Recording