Calibrated Trust: Human and AI Reliability Judgement for Safer Decisions

About the research project

AI tools are increasingly embedded in decisions that affect safety, services, productivity, and public trust. Harm often arises not only when AI systems are wrong, but when humans rely on them at the wrong moments, or when systems fail to indicate the limits of their own reliability. This project reframes calibrated trust as a two-sided interaction: people need the judgement to know when to rely, question or override AI, and AI systems need better ways to recognise and communicate when their outputs are reliable, uncertain or contextually unsafe. 

The core research question is: can the judgement of when to rely on AI be deliberately calibrated on both sides of the interaction, by training people to rely well and by measuring how well AI systems recognise and communicate their own reliability, and does better joint calibration reduce harm and improve decisions? 

The project will integrate human-centred AI, psychology, decision science, human factors, responsible AI and applied workflow transformation. It will develop methods for measuring calibration in realistic decision contexts, model the human and system signals that shape reliance, and test interventions that improve both human reliance behaviour and system reliability communication. The work may examine safety-critical, regulated and operationally complex settings, as well as AI-enabled business processes where sustained process improvement depends on appropriate reliance. 

The contribution is both scholarly and practical. Academically, the project will provide a defensible account of joint calibration in human-AI interaction, including behavioural measures of reliance and system-facing measures of reliability recognition and communication. In practice, it will produce tools, training approaches, and design guidance that organisations can use to reduce harm, improve decision-making, and embed AI into their work without outsourcing judgement or accountability. The project will also provide opportunities for industry internships, engagement with relevant organisations, and exposure to the organisational and practical contexts in which AI-supported decisions are made.

The project aims to produce at least two high-quality journal or conference papers. Potential topics include:

• frameworks and measures for jointly calibrating human reliance and AI reliability communication;
• training and system-design methods that improve reliance decisions and reduce harm; and
• evaluation of joint calibration in safety-critical, regulated, or organisational decision contexts.

Potential publication venues include the International Journal of Human–Computer Studies, Human-Centric Intelligent Systems, and conferences such as ACM CHI and AAAI/ACM AIES. The supervisory team will provide research and writing support throughout the publication process.

Primary supervisor

Meet Doctor Wenli Yang

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

• a living allowance stipend funded by University of Tasmania of $34,315 per annum for 3.5 years
• a relocation allowance of up to $2,000
• a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

A tuition fee offset may be offered to eligible international applicants following competitive assessment 

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Other funding opportunities and fees

For further information regarding other scholarships on offer, and the various fees for undertaking a research degree, please visit our Scholarships and fees on research degrees page.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Ensure your eligibility for the scholarship round by referring to our Key Dates.

Additional eligibility criteria specific to this project/scholarship:

• Applicants must meet UTAS HDR admission requirements for a PhD or relevant HDR degree.
• The project would suit an applicant with a background in information systems, human-computer interaction, artificial intelligence, psychology, human factors, behavioural science, cognitive science, decision science, data science, organisational studies, or a closely related field.
• The candidate should be comfortable working across human behaviour, AI systems, empirical research, and applied partner settings.

Selection criteria

The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

• Strong academic preparation in ICT, psychology, human factors, human-computer interaction, behavioural science, AI, information systems, or a related field. 
• Capacity to design empirical studies with human participants, including experiments, scenario-based tasks, surveys, interviews, behavioural measurement or mixed-methods evaluation. 
• Interest in responsible AI, human-AI interaction, AI safety and harm prevention. 
• Ability to engage with industry or applied partners and translate research findings into practical outputs for organisations. 
• Strong written communication skills and capacity to work ethically with human participants.

Additional desirable selection criteria specific to this project:

• Experience with experimental design, statistics, psychometrics, qualitative methods, usability testing, or evaluation methodology. 
• Familiarity with AI systems, machine learning, decision-support systems, reliability estimation, uncertainty communication, or explainable AI. 
• Experience with organisational change, workflow redesign, digital transformation, training design or capability uplift. 
• Experience working with industry, government, research organisations, start-ups or safety-critical sectors. 
• Interest in producing practical frameworks, design guidance, measurement tools, or training resources. 

Application process

1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
2. Contact Doctor Wenli Yang to discuss your suitability and the project's requirements; and
3. In your application:
   • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
   • Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
4. Apply prior to 1 October 2026.

Full details of the application process can be found under the ' How to apply ' section of the Research Degrees website.

Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

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