Human-Centered AI
1) AI for Improving Scientific Discovery, Analysis and Interpretation
This research aims to improve human-AI collaboration in scientific discovery, analysis, and interpretation, with a focus on AI explainability. It explores how AI can support researchers by providing transparent, interpretable insights, helping them better understand complex data and make informed decisions in scientific inquiry.
Responsible AI
1) Studying Annotator Bias
AI systems inherit biases from human annotations, shaped by cognitive and societal factors. My research investigates annotator bias—how personal perspectives influence AI training data—and explores methodologies to mitigate these biases for more fair and reliable AI systems.
2) Harms in Text-to-Image Generation with focus on Global South
With the increasing integration of advanced generative models in real-world systems, concerns have emerged about these models' biases, particularly favoring majority demographics across sociodemographic lines. This work offers a critical review of research addressing these social harms and presents open-ended research questions to stimulate further inquiry and guide future efforts in mitigating biases in AI systems.
3) Nationality Bias Detection in Text Generation
This research investigates nationality biases in NLP models and their impact on fairness and justice in AI systems. Using a mixed-methods approach, the study quantitatively measures bias in AI-generated articles and qualitatively analyzes its implications through interviews. Findings show that biased NLP models amplify societal biases, potentially leading to harm in sociotechnical settings. The qualitative analysis reveals readers' altered perceptions of countries influenced by biased articles. The research emphasizes the importance of addressing biases in AI systems, correcting them to ensure ethical and equitable deployment, and recognizing the role of public perception in shaping AI's societal impact.
Social Media for Civic Awareness
1) Understanding the Workings of 911 Centres during Covid-19
Implementing these COOP plans required PSAPs to decentralize operations by moving staff out of “one big room” to multiple workspaces across primary and alternative facilities, including people’s homes. Unsurprisingly, our interviews highlight disruptions to shared physical spaces, or social infrastructures, caused by outbreaks of infectious disease like COVID-19, and the vulnerability of essential functions when performed by people in centralized, collocated workspaces.
2) Using Twitter for Crisis Response
During the pandemic social media gained special interest as it went on to become an important medium of communication. This made the information being relayed on these platforms especially critical. In our work, we aim to explore identification of fake news, misinformation and using Twitter for real-time information sharing. Our study is useful in establishing the role of Twitter, and social media, during a crisis, and more specifically during crisis management.
3) What’s Political on TikTok?
This study explores user exposure to political content on TikTok through a custom tool that tracks videos viewed by 358 participants and captures their perceptions. Findings show political content makes up about 13.7% of users' feeds, with demographics—particularly age, education, and political views—being key factors in exposure levels.
Learning Community Designs
1) Applying Social Constructivist Theories in Adaptive Learning
My thesis work revolves around applying social constructivist theories in the world of adaptive learning. With this work, I aim to develop adaptive learning systems that not only account for student performance but also further the goal of social learning as described by Bandura. This should be useful in the context of online learning both as motivation as well as for improved learning.
2) We Are One: Study on Distributed Communities
Options for students to learn and connect have diversified in recent years, with online resources playing an increasing role. Nonetheless, students want to feel a sense of community with their peers and instructors; We explore the feelings of community among students studying at a geographically distributed university.
3) AI Curriculum Design
Many AI programs aim to meet industry demand, but gaps remain between education and job needs. Our work examines industry expectations through the lens of AI experts.
4) YouTube User Data Modelling
YouTube is one of the most popular websites. To better understand the characteristics and impact of YouTube on education, we analyzed a popular YouTube channel. Our analysis provides valuable information that can have major technical and commercial implications in the field of education. We perform in-depth time-series analysis of the channel data to reveal the trend, seasonality and temporal pattern for the educational videos on YouTube.
Plant Village (NLP supported learning tool for African Farmers)
PlantVillage has developed a triple A model (Algorithmic Agricultural Advice) that works to increase the yield and profitability for millions of farmers. It is our goal to reach hundreds of millions in partnership with an ecosystem of farmer facing organizations and the farmers themselves. Our algorithms come from our integration of AI, satellite technology and our unique field force (the Dream Team).