PhD scholarship opportunity: Bias in generative AI-driven decision systems

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Join to apply for the PhD scholarship opportunity: Bias in generative AI-driven decision systems role at Monash University

PhD scholarship opportunity: Bias in generative AI-driven decision systems

PhD scholarship opportunity: Bias in generative AI-driven decision systems

Job No.: 680086

Location: Caulfield campus

(with possible collaborations at the Clayton campus as needed)

Employment Type: Full-time

Duration: 3-year fixed-term appointment

Remuneration: The successful applicant will receive a tax-free stipend, at the current value of $36,063 per annum 2025 full-time rate, as per the Monash Research Training Program (RTP) Stipend www.monash.edu/study/fees-scholarships/scholarships/find-a-scholarship/research-training-program-scholarship#scholarship-details

This opportunity invites applications from outstanding domestic and international candidates who are interested in undertaking a PhD focused on bias propagation in generative AI-driven decision systems and its implications for trust, governance, and regulatory compliance. The project is part of an interdisciplinary research team led by experts from the Opportunity Tech Lab within the Business School and the Faculty of IT at Monash University.

The PhD candidate will be working with a team of distinguished researchers:

• Professor Charmine Hartel (Department of Management, Monash Business School; Director, Opportunity Tech Lab)
  
  
• Associate Professor Kristian Rotaru (Department of Accounting, Monash Business School; Deputy Director, Opportunity Tech Lab)
  
  
• Dr Mor Vered (Department of Data Science & AI, Faculty of IT)
  
  
• Dr Estelle Wallingford (Department of Business Law & Taxation, Monash Business School; Opportunity Tech Lab)
  
  

The Opportunity

This project addresses a pressing public policy and social issue: the propagation of bias in generative AI systems and its impact on human decision-making, trust, and regulatory design. As generative AI is increasingly deployed in high-stakes decision-making settings – such as financial markets, healthcare, and legal practice – its capacity to produce biased, yet seemingly neutral, outputs poses a significant risk to fairness, accountability, and public trust.

A core focus of the project is understanding how AI-generated explanations influence trust formation, cognitive effort, and user behaviour, including the risk of over-reliance or inappropriate scepticism when explanations are misleading or hallucinated. The candidate will contribute to the development of empirically validated methods for identifying and mitigating such effects.

The research will involve experimental studies, neurophysiological methods (e.g., eye-tracking, pupillometry), cognitive modelling, and regulatory analysis to assess how algorithmic explanations shape human judgement and how existing legal and ethical frameworks align with the evolution of generative AI.

Essential Skills And Experience

• A background in a relevant field such as behavioural science, cognitive science, data science, psychology, human-computer interaction, law, or a related discipline
  
  
• Demonstrated experience in empirical research (quantitative, qualitative, or mixed methods)
  
  
• Strong written communication skills
  
  
• A clear interest in interdisciplinary research on AI, decision-making, governance and ethics.
  
  

Desirable skills

• Experience with experimental design and behavioural data collection
  
  
• Familiarity with generative AI systems, explainability (XAI), or algorithmic fairness
  
  
• Skills in statistical analysis and/or coding (e.g., R, Python, C++)
  
  
• Exposure to neurophysiological measurement methods (e.g., eye-tracking, pupillometry)
  
  
• Interest or training in technology law, digital regulation, or AI ethics
  
  
• An ability to engage in legal research, including familiarity with legislation, regulations and case law, would be advantageous but is not essential for the role
  
  

To Apply

This position has a two-stage selection process:

To apply for this position, please submit an Expression of Interest (EOI) via the Faculty of Business and Economics at Monash University. Please direct your EOI to Associate Professor Kristian Rotaru, kristian.rotaru@monash.edu.

Your EOI should include a brief research proposal (maximum 3 pages) that clearly aligns with the project's focus on bias in generative AI-driven decision systems. The proposal should outline your interest in the topic, specify the theoretical and methodological approaches you are considering, and describe how your academic background and prior experience equip you to contribute to the project's objectives. Where possible, please highlight relevant skills – such as experimental design, data analysis, neurophysiological methods, or legal/regulatory analysis – that can be directly cross-referenced against the project's interdisciplinary goals. The proposal should also reflect your enthusiasm for working at the intersection of business, IT, cognitive science, and law.

Candidates shortlisted from the EOI stage will be invited to discuss their ideas prior to submitting a formal PhD application to the Faculty of Business and Economics. The successful candidate will enrol in an interdisciplinary cross-faculty project, with the PhD degree to be awarded by the Faculty of Business and Economics upon completion of the project and the Monash doctoral requirements.

Enquiries: Associate Professor Kristian Rotaru, kristian.rotaru@monash.edu, +61 3 9903 4567

Supporting a diverse workforce

For instructions on how to apply, click on the "Apply" button above for the full position description and instructions on how to apply for Monash jobs.Seniority level

• Seniority levelInternship

Employment type

• Employment typeFull-time

Job function

• Job functionResearch
• IndustriesHigher Education

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