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Proposer
Neamat El Gayar
Title
Explainability in AI and Deep Learning
Goal
Compare and text models
Description
The objective is to compare and implement different explainability models on a sample image data set. Interpretable AI, or Explainable Machine Learning (XML), usually refers to model that can be explained by a human or it decisions can be interpreted. The main focus is usually on the reasoning behind the decisions or predictions made by the model to be more transparent. This is of particular importance for interpreting medical or security related decisions in machine learning models. XAI attempts to unravel the "black box" tendency of machine learning and attempt to explain why a model arrived at a specific decision. Some resources: Data set: https://www.kaggle.com/c/aptos2019-blindness-detection/data Readings: https://aclanthology.org/2021.eacl-demos.17/ https://arxiv.org/pdf/1910.10045.pdf
Resources
Background
Url
Difficulty Level
Easy
Ethical Approval
None
Number Of Students
0
Supervisor
Neamat El Gayar
Keywords
Degrees
Master of Science in Artificial Intelligence
Master of Science in Data Science
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