Enabling Human-In-The-Loop Interpretability Methods of Machine Learning Models: The Case of Bird Species Identification

To study how to involve the end-users in the development of machine learning explainability, this project has chosen the context of bird species identification. It intends to develop a platform where the end-users can learn bird knowledge while contributing to building the explainability of machine learning models. Among all the methods that...

One Step Ahead: A weakly-supervised approach to training robust machine learning models for transaction monitoring

In recent years financial fraud has seen substantial growth due to the advent of electronic financial services opening many doors for fraudsters. Consequently, the industry of fraud detection has seen a significant growth in scale, but moves slowly in comparison to the ever-changing nature of fraudulent behavior. As the monetary losses...

Multi-Level Fairness Framework: A Socio-Technical framework for Fairness Requirements Engineering in Machine Learning

Machine Learning models are begin increasingly used within the industry such as by financial institutions, governments and commercial companies. In the past few years, there have been several incidents where these ML models show discriminatory behavior towards particular groups of people, leading to unfair decisions that can have negative...