Seeking students to develop new approaches to fairness, actionable explainability or socially considerate evaluation of ADM in recommender, search, or other ML based systems.
This project is funded by the ARC Centre of Excellence for Automated Decision-Making and Society.
$31,885 per annum for three years with a possible extension of six months (full-time).
To be eligible for this scholarship you must:
To apply, please submit the following documents to Jeffrey Chan via email@example.com
For international applicants, evidence of English proficiency may be required.
Once approved, prospective candidates will be required to submit an application for admission to the PhD (Computer Science) program (DR221) as per instructions on this website.
Scholarship applications will only be successful if prospective candidates are provided with an offer for admission.
Applications are open now.
Applications will close once a candidate is appointed with intention to start.
Potential projects could encompass one of the following areas and venues that the research could be published in:
Creating a next generation recommender system that enables equitable allocation of constrained resources. Many recommender systems now suggest items or services drawn from resource constrained environments such as tourist destinations. Unlimited use disrupts the limited capacity of such resources; hidden locations become tourist destinations and neighbourhoods become hotel complexes. Recent research has addressed the problem of building recommender systems that are fair to their registered users, but this comes at the profound risk of being unfair to others (so-called third parties). The incorporation and modelling of such third-party views is a critical omission in existing systems. Our next generation recommender system will consider the preferences, tolerances, and social norms of the system's users as well as its third parties and nonusers.
Studying and developing new approaches that combines fairness, privacy and legal guarantees for ADM systems, such as recommender and machine learning based systems. It takes a multi-disciplinary approach and although focused on the transportation focus area, can potentially be applicable in other areas. The project is divided into three work packages, roughly one year in length each. For a mid-point review of the project, we would aim to demonstrate results on formulating and testing different fair routing policies in route recommendation.
Acknowledgement of Country
RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Luwaytini' by Mark Cleaver, Palawa.