PhD Scholarship in bias propagation in generative AI-driven decision systems and its implications for trust, governance, and regulatory compliance
PhD Scholarship in bias propagation in generative AI-driven decision systems and its implications for trust, governance, and regulatory compliance
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1 Vacancy
Job Description
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 $36063 per annum 2025 full-time rate as per the Monash Research Training Program (RTP) Stipend 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.
Your EOI should include a brief research proposal (maximum 3 pages) that clearly aligns with the projects 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 projects 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 projects 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
Supporting a diverse workforce
Monash University recognises that its Australian campuses are located on the unceded lands of the people of the Kulin nations and pays its respects to their elders past and present.
Employment Type
Student
