About the Project
When humans label data to train AI models, their own biases and stereotypes may be reflected in the data, which consequently appear in the resulting trained models leading to unfair, biased, and non-transparent decisions. In collaboration with the Queensland Police Service (QPS), this project focuses on integrating fairness into learning algorithms used in the context of policing services and tasks and aims to observe if this leads to improved outcomes and experiences. The approach will include the development of human-in-the-loop AI, where humans help to increase transparency of automatic decision-making process, e.g., by generating natural language explanations on why a specific amount of police resources is required in a certain suburb.
About the Candidate
This is an opportunity for a highly motivated student to join the CIRES project team, collaborating closely with leading experts in the Queensland Police Service (QPS) to generate more transparent, fair, and trustworthy decision-support systems driven by data and controlled by humans. This scholarship is one of three CIRES projects with QPS related to the responsible use of sensitive data assets.
The successful candidate is expected to have a good background in data science, data analytics, or machine learning and will develop novel bias tracking, management, and reduction method over the entire Artificial Intelligence pipeline: from data collection and curation to model training and deployment with end users.
This project seeks to develop a strong and capable future leader who can undertake data analysis in a data-sparse environment, with the proposed model and research tasks able to be adapted and applied to other human-in-the-loop tasks.
For this position, CIRES is seeking a candidate with an understanding of concepts from applied statistics/probability, machine learning, algorithms and complexity, and human-computer interaction. This project also requires proficiency in the Python programming language, and machine learning software packages such as Pytorch or Tensorflow.