Fairness Under Inequality: Do AI Agents Allocate Resources Differently to People Facing Disability or Disadvantage?

Overview

Decisions about money, employment, trust and social support are often shaped by perceptions of fairness, need, deservingness, visibility and stigma. People may respond differently when they know that another person is living with a physical disability, neurodivergence, low income, unemployment, homelessness, addiction, a criminal record or another form of disadvantage. These differences can affect how resources are allocated, who is trusted, what wages are offered and whether people are treated as deserving of support or opportunity.

As AI systems are increasingly used to support decisions in employment, welfare, finance, health and public services, it is important to understand whether AI-generated reasoning and decisions reproduce, amplify or challenge these social patterns. This interdisciplinary project combines behavioural economics, disability studies, social psychology, AI ethics, computational social science and data analysis. It uses an existing synthetic-agent dataset in which AI agents completed classic economic games involving different recipient characteristics and forms of inequality.

The study includes classic experimental economic games such as the dictator, ultimatum, trust and wage setting games. These games provide controlled settings for examining generosity, fairness, trust, acceptance thresholds and employment-related decisions. For example, the project can investigate whether an AI agent allocates more money to a recipient described as a wheelchair user than to one described as unemployed, homeless, autistic, experiencing addiction or having a criminal record. It can also examine whether different types of inequality generate distinct patterns in trust or wage offers.

The purpose is not to make claims about the real preferences, capabilities or deservingness of people in these groups. Instead, the project studies how AI models react to social categories and whether those reactions may reflect stereotypes, empathy cues or assumptions about productivity and reliability. This can provide valuable evidence for the responsible development and evaluation of AI systems used in socially sensitive contexts.

Research engagement

The student will:

• Conduct a review existing literature to identify gaps and contextualise the current study;
• Work on data using quantitative data analysis techniques; and
• Document the findings of the study, interpreting the data and drawing conclusions based on the analysis.

Research activities

The student will work with Professor Benno Torgler and members of the behavioural economics/AI research team at the ARC BITA Centre. They will gain practical experience in Stata, Python or R, reproducible data cleaning, regression modelling, visualisation, research documentation and academic writing.

• The project aims to produce a draft manuscript that includes the following:
  • Introduction: Overview of the study’s background and significance.
  • Literature Review: Review of the existing research literature and identification of gaps.
  • Research Aims/Objectives and Questions/Hypotheses: Clear articulation of the study’s aims/objectives and research questions/hypotheses.
  • Methodology: Description of the data collection process and analysis
  • methods.
  • Results and Discussion: Presentation and interpretation of findings.
  • Conclusions and Future Work: Summary of key insights and
  • recommendations for future research.
  • Reference List/Bibliography
  • Appendices (optional)
• Generate a brief, 2-3 slide presentation to present your research at the Faculty of Business and Law VRES Showcase to conclude the program.
• (Optional) The student will be eligible to present the findings of their research to an audience of 100+ academics and industry partners in the annual BITA conference in February/March 2027, provided the student is keen and interested to.
• (Optional) The ultimate goal is to co-author and submit the manuscript with the student to an academic journal, provided the student is keen and interested to. However, it should be noted that the primary deliverable is a final draft manuscript. As it is not certain that the student will be paid beyond the completion of the VRES program in early 2027, any further work on the paper would be entirely voluntary and optional for the student.

Research skills

They will gain practical experience in Stata, Python or R, reproducible data cleaning, regression modelling, visualisation, research documentation and academic writing.

Outcomes

• The project aims to produce a draft manuscript that includes the following:
  • Introduction: Overview of the study’s background and significance.
  • Literature Review: Review of the existing research literature and identification of gaps.
  • Research Aims/Objectives and Questions/Hypotheses: Clear articulation of the study’s aims/objectives and research questions/hypotheses.
  • Methodology: Description of the data collection process and analysis
  • methods.
  • Results and Discussion: Presentation and interpretation of findings.
  • Conclusions and Future Work: Summary of key insights and
  • recommendations for future research.
  • Reference List/Bibliography
  • Appendices (optional)
• Generate a brief, 2-3 slide presentation to present your research at the Faculty of Business and Law VRES Showcase to conclude the program.
• (Optional) The student will be eligible to present the findings of their research to an audience of 100+ academics and industry partners in the annual BITA conference in February/March 2027, provided the student is keen and interested to.
• (Optional) The ultimate goal is to co-author and submit the manuscript with the student to an academic journal, provided the student is keen and interested to. However, it should be noted that the primary deliverable is a final draft manuscript. As it is not certain that the student will be paid beyond the completion of the VRES program in early 2027, any further work on the paper would be entirely voluntary and optional for the student.

Skills and experience

• Suitable for students in economics, psychology, data science, social science, IT, health or public policy;
• Some proficiency in data entry, data analysis, and statistical techniques is desirable;
• Experience using software such as Stata, R and Python would be beneficial for this role; and
• Students should be comfortable engaging respectfully with disability, inequality and stigma-related research.

Start date

2 November, 2026

End date

19 February, 2027

Location

QUT Gardens Point campus (Z Block, Level 7), with some work able to be completed online/remotely subject to mutual agreement between the student and their VRES supervisor.

Additional information

Existing simulation data, variable documentation, starter code, readings and regular supervision will be provided. Students will complete their own self-guided preparatory readings on experimental economic games, disability and stigma-based research and responsible interpretation of synthetic agent findings.

Keywords

• Behavioural Economics
• Experimental Economics
• Generative AI
• Synthetic Agents
• Inequality, Fairness and Trust
• Resource Allocation

Contact

Prof. Benno Torgler

+61 7 3138 2517

benno.torgler@qut.edu.au