Interpretable AI Services for Enhanced Air Quality Forecasting - Ketan Shahapure; Samit Shivadekar; Bhrigu Bhargava (International Journal of Scientific Research in Science, Engineering and Technology, 2024)
Most of the Machine Learning (ML) models used these days establish a complex relationship between the in- dependent variables (X) and dependent variable (y). Without understanding the relationship, we risk introducing undesirable features into the predictions. Biased collection of the data, used to build the model, might bolster these undesirable features. The model might soon become unfit for its intended tasks. This project tries to get deeper insights into such black box machine learning models by looking into various ExplainableAI (XAI) tools and provide it as a service to users. These tools when used in conjunction can make complex models easy to understand and operate for the end-user. Specifically, the tools used would help the user of the machine learning model interact with it and monitor how it behaves on changing certain aspects of the data. To facilitate the better understanding of the achieved outcome, this project uses a weather data-set which is used to classify the air quality.
Keywords: Explainable AI; Machine Learning; Classifi- Cation; Service Oriented Computing