PhD Scholarship in Automated Decision-Making and Human Computer Interaction

Seeking students to develop new approaches to fairness, actionable explainability, or socially considerate evaluation of ADM in recommender, search, or other ML based systems.

PhD opportunity to work on a project 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: 

  • Have a first-class Honours in computer science or an equivalent
  • Have strong computational, programming, algorithms and data analysis skills
  • Provide evidence of adequate oral and written communication skills
  • Demonstrate an ability to work as part of a multi-disciplinary research team
  • Meet RMIT University’s entry requirements for the Higher Degree by Research programs
  • Preferably be an Australian citizen, or Australian permanent resident .

To apply, please submit the following documents to Mark Sanderson (

  • A cover letter (research statement) 
  • A copy of electronic academic transcripts 
  • A CV that includes any publications/awards and the contact details of 2 referees
  • Thesis or research reports 

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).   

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:      

Measuring and quantifying users’ cognitive biases and fairness perceptions when interacting with information access systems, including search engines, intelligent assistants, and recommender systems. These systems are often embedded in Automated Decision-Making (ADM) processes, and are designed, evaluated, and optimised by defining frameworks that model the users who are going to interact with them. These models are typically a simplified representation of users (e.g., using the relevance of items delivered to the user as a surrogate for system quality) to operationalise the development process of such systems. A grand open challenge is to make these frameworks more complete by including new aspects such as fairness --that are as important as the traditional definitions of quality— as well as better mechanisms to measure and quantify cognitive biases, which would inform the design, evaluation, and optimisation of such systems embedded in ADM processes.      

Capturing third-party and non-user opinion on multi-party recommender systems and 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 non-users. We are interested in capturing and modelling third parties' views and attitudes, incorporating them into the recommender system and evaluating how parties react in real-world applications.      

The projects largely involve Human-Computer Interaction research, where the student will implement prototype solutions and conduct large scale user studies with both quantitative and qualitative methods. Throughout the PhD, the student is expected to engage with the broader HCI community and publish at top venues such as CHI, IMWUT, and CSCW. 

This scholarship will be governed by RMIT University's Research Scholarship Terms and Conditions.

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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.