Ritika Pandey.
Ritika Pandey uses data science and artificial intelligence to make complex information more accessible.
As a research assistant professor in data science for social impact at the Boston College School of Social Work, Pandey applies her background in computer science and machine learning to turn raw data into information that residents and policymakers can use to better understand their communities.
Much of her work is conducted through the Gateway Initiative, the School’s long-term commitment to building capacity across Massachusetts’ 26 Gateway Cities—midsize urban centers that anchor regional economies around the state.
In the Gateway City of Lawrence, Massachusetts, Pandey helped design an interactive dashboard that turns publicly available police incidents into visualizations of crime trends.
She and her colleagues have also created a chatbot for residents to ask questions about the data, making complex information easier to understand.
We asked Pandey to discuss how she is using AI and data science to support communities, why human oversight remains essential, and how she sees these technologies shaping the future of social work.
Your research lies at the intersection of machine learning, natural language processing, and large language models. What drew you to applying those fields to social impact?
I received my PhD in computer science, and much of my research has focused on data science and machine learning for social science applications. In particular, my dissertation centered on rewiring police officer training networks to reduce forecasted use of force, so it just felt like a natural transition from that to my current position.
How would you describe the work you’re doing at the Boston College School of Social Work?
It’s mostly applied data science. I was hired for the Gateway Initiative, the School’s long-term commitment to building capacity across Massachusetts’ 26 Gateway Cities—midsize urban centers that anchor regional economies around the state. A lot of the work that I do is using publicly available data and providing insights from that. Sometimes it’s also providing nonprofits with the help that they need—data collection, analysis, visualization.
What project best illustrates how you’re using data science through the Gateway Initiative?
Most of the impact that we have had through the Gateway Initiative is providing information in an accessible way. There’s so much data out there, but it’s hard to analyze, visualize, and understand, so that’s where we come in.
In particular, we’re working on a data-driven initiative aimed at better understanding police incidents through the analysis of publicly available police data. The Lawrence Police Department publishes every call for service it receives, so we downloaded the entire dataset, which includes information from 2018 through 2024, and now we’re working on updating it every day.
Using this data, we designed an interactive dashboard for people to better understand incidents in their community. Where are these incidents happening? Are there specific types of incidents happening in specific areas of their city? What neighborhoods are more prone to motor vehicle incidents of certain types?
This information is also helpful for policymakers. Let’s say they notice a hotspot for motor vehicle incidents. They might examine whether road conditions are contributing to those incidents and determine whether certain roads need to be fixed—going to the root cause of the problem.
What kinds of information can people explore through the dashboard?
The dashboard has 14 or 15 different incident categories. We started with motor vehicle incidents because they are the most common, but it also includes violent incidents, drug-related incidents, and public disturbances.
We also have a heat map—you can click and see exactly where incidents are happening. And you can select different years: Do you want to see how incidents have changed from 2018 up until 2024? You can do that.
You can also overlay different types of data, including census data, unemployment data, and education data. We also have points of interest. So you can overlay where, for example, schools are, where grocery stores are, and where nonprofits are.
How has feedback from the Lawrence community shaped the work?
This past summer, we met with nonprofit staff and community leaders, who provided feedback on the tools that we’ve developed, which was very helpful.
Our next step is training community members to use these tools and asking them to help us understand what else would be useful for them. We aren’t from Lawrence, and we recognize that we are not the community. We need to understand what residents’ needs are and how we can be helpful in meeting them.
You’ve incorporated AI into the public safety project. How does that work?
We recently created a chatbot so that community members can ask questions in plain English. The good thing is that it’s grounded in this incident data. So if it doesn’t know anything about the question you ask, it will tell you, “I don’t know anything about it.”
Let’s say you ask: “In 2024, how many drug-related incidents happened?” On the back end, the chatbot will convert your question into code and provide a clear answer in plain English.
What impact do you hope the dashboard will have in Lawrence?
The dashboard is a learning tool. We want residents to use it to gather information to take to their representatives and present it as evidence for where change might be needed. If community members are getting some value out of it, I think that’s good enough for us.
A lot of people might not immediately see the connection between AI, data science, and social work. Where do you see that connection?
I think there’s a big connection. I’m a huge proponent of making things accessible for people, and I think AI is making things easier for people to understand.
For the dashboards that we’ve created, community members have asked whether we can design chatbots to go with them. They’ve told us that they don’t necessarily understand what the numbers on our dashboards mean, so having a chatbot answer questions about these figures helps people understand what they’re seeing.
As long as we’re using reliable data and not providing access to people’s private information, AI can help people who aren’t technical learn more about their community and have a bigger social impact.
AI and data science raise concerns about bias, privacy, and fairness. How do you approach those issues?
I’m a huge proponent of keeping humans in the loop. We can’t have AI do all of our work—we need to have people evaluating its output as we go.
Here’s an example: We have a dashboard that’s pulling data from a range of websites for nonprofit organizations in Lawrence. On the back end, we’re using Gemini to summarize the information.
But we don’t trust everything that Gemini is gathering, so we have a student who’s evaluating 15 percent of its output. We identify where the tool is performing poorly and design better prompts to improve its performance. I think there needs to be a human involved. We can’t leave everything up to the machine.
What excites you most about the future of data science, social work, and community-engaged research?
Oh, I’m so excited. I see the world as an oyster. People are now able to use AI tools in their daily lives to better understand data, ask more questions, and question things that they otherwise might have taken for granted.
How do you see these tools shaping social work practice and research over the next decade?
There are so many avenues that haven’t been looked into yet. But these AI tools are providing an inroad for us to explore data in new ways. There’s so much information out there—and only so much that people can do.
AI tools have made our lives so much easier. A task that once took a month now takes a week. Things have gotten faster—and I’m very excited about how these tools will continue to evolve.
What gives you confidence about where these technologies are headed?
I’m very excited for the future, and I’m an optimist. I feel like there’s so much good that can come from using these technologies. And I can’t wait for people to keep exploring them and use them for good.
This story is part of our series highlighting how students, faculty, and staff at BCSSW are putting AI into practice across teaching, research, and clinical training.
