Over the past few years, Machine Learning (ML) and deep learning (DL) models have found applications in several fields such as healthcare, finance, and e-commerce. These models, however, may be very powerful but tend to behave as black boxes, making it almost impossible to understand the reasoning behind the predictions. This has given rise to serious issues in some fields of application, particularly those dealing with human life, such as cybersecurity and medical decision-making, where knowing why a model gave a certain output may be as paramount as the output's accuracy.
AlgoInsight aims to tackle this challenge by introducing methods for enhancing the transparency, explainability, and interpretability of ML and DL models. Specifically, we explore the role of various model components and how they influence overall performance. The project centers on improving transparency by visualizing model decisions, analyzing features, and explaining why certain predictions are made. It would be important for researchers, developers, and industries that depend on artificial intelligence (AI) for making critical decisions so that they can trust their models and improve them more easily.
By providing deeper insights into the inner workings of AI models, AlgoInsight seeks to bridge the gap between model performance and user understanding, ensuring that AI systems are not only accurate but also interpretable and reliable.