A data auditing tool based on explainable artificial intelligence

• Main Author: Wong, Jing Yao
• Other Authors: Yu Han
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Computer science and engineering::Software::Software engineering
• Online Access: https://hdl.handle.net/10356/156661
• Institution: Nanyang Technological University

With the rapid growth of Information Technology and the number of internet users increasing each year, the amount of data being generated each day on the internet is increasing. Artificial intelligence requires enormous amounts of data to learn and improve its decision-making process. The increase of data and data is much more available, this leads to the popularity of data-hungry technologies such as artificial intelligence. It is estimated that the market for artificial intelligence is $62 billion in 2020 and is expected to expand at a compound annual growth rate of 40.2% from 2021 to 2028 [1]. In recent years, artificial intelligence has impacted the daily lives of many. Artificial intelligence is everywhere. For Example, using Face ID to unlock the smartphone, using google search, Recommendation Systems such as Amazon product recommendations and Netflix show recommendations and self-driving cars [2]. However not all data are treated evenly, some data are more useful than others which helps to improve artificial intelligence decision-making processes while others might worsen artificial intelligence decision-making processes. By using explainable artificial intelligence, Hypergradient Data Relevance Analysis for Interpreting Deep Neural Network (HYDRA), we can gauge the usefulness of the data. A higher positive contribution result indicates that the data is useful in the training of its decision-making processes while the lowest negative contribution indicates that the data worsens its decision-making processes when training [3]. The objective of this project is to demonstrate the use of Hypergradient Data Relevance Analysis for Interpreting Deep Neural networks using Web applications