TeaPot

At Teapot, our mission is to delve into the cutting-edge domain of the disagreement problem and explore the latest evaluation metrics introduced in the field. In 2023, we embarked on a research journey to unravel the intricacies of Human-Machine Learning (ML) interaction using Explainable Artificial Intelligence (XAI).

Teapot is dedicated to advancing the understanding of the disagreement problem and its implications in the realm of Human-ML interaction. Our research centers around exploring the barriers inherent in this interaction through the lens of Explainable AI, utilizing the most recent methodologies and insights available. Join us on this exciting journey as we strive to push the boundaries of knowledge and contribute to the ever-evolving landscape of artificial intelligence.

Our study relies on the DonorsChoose dataset (2014-2018), encompassing Projects.csv, Donations.csv, Schools.csv, and Teachers.csv. We've amalgamated these datasets, yielding 1,110,015 unique projects. Our focus extends to teacher donations, particularly analyzing four months post-project posting. We calculate total donations, determining the percentage of the project cost covered. Currently, we haven't explored converting text columns to NLP features. This dataset exploration forms the foundation for our research, providing insights into project funding dynamics and community engagement within the realm of Explainable AI.

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