Exclusive by Design: Algorithmic Bias and Marginalisation in AI-Augmented Business Processes

Overview

Inclusive process design has long been an aspirational goal in BPM, yet the integration of AI into process automation introduces new and systemic risks of exclusion. AI systems trained on historical data embed the structural inequities already present in those datasets, and when deployed within organisational processes such as hiring, welfare, credit, and health triage, these biases are operationalised at scale. Critically, the façade of algorithmic objectivity can mask discrimination, leading both process designers and end-users to uncritically trust biassed outcomes. Research confirms that marginalised workers (by race, gender, age, and disability) are disproportionately impacted by automated decision-making embedded in business processes. Yet the BPM discipline has largely treated process design as a technical and efficiency-oriented endeavour, giving insufficient attention to participatory design, equity audits, or the lived experiences of process participants who are not at the design table. This represents a critical blind spot in both BPM theory and practice.

RQ: In what ways do AI-augmented business processes encode and reproduce structural inequities, and how can inclusive, participatory process design principles be embedded into the BPM lifecycle to mitigate these harms?

Research engagement

Literature review, experimentation  (event log analysis)

Research activities

Literature Review to synthesise studies on algorithmic bias, inclusive design, and BPM participation literature across IS, HCI, and critical management studies, and GEDSI. Participatory Action Research Co-design workshops with marginalised process participants; iterative prototype testing and feedback cycles. Process Mining / Audit Empirical analysis of real process event logs to surface statistically disparate decision outcomes by demographic segment. Instrument Development Design and validation of an equity audit checklist for use across BPM lifecycle phases.  Case Study Documentation Rich, interpretive documentation of two or three organisational cases where process automation has produced exclusionary outcomes

Research skills

This topic demands competence in sociotechnical systems analysis and the ability to integrate social theory (e.g., Critical Theory, Structuration Theory, or Feminist Technology Studies) with process analysis methods. The student must develop practical skills in process mining and bias auditing, applying tools like ProM or Celonis to detect disparate outcomes based on demographic attributes embedded in event logs. Strong participatory research skills are essential, including community engagement, co-design facilitation, and the ethical management of vulnerable participant groups. Familiarity with the WEF Blueprint for Equity and Inclusion in AI and PAI participatory guidelines provides important normative scaffolding

Outcomes

1. An equity audit instrument for BPM practitioners to identify bias risk points across the BPM lifecycle
2. A Participatory Process Design Framework that integrates inclusive design principles into BPM methodology (discovery, modelling, implementation, monitoring)
3. Empirical case study evidence of bias operationalisation in specific process contexts
4. A practitioner guide and a conference or journal article suitable for AIS and BPM conferences and journals such as Government Information Quarterly, Information & Management, or Business Process Management Journal, AJIS.

Skills and experience

• Applied knowledge in process mining tools and their extension into equity/bias auditing
• Participatory and action research design skills, including facilitation, stakeholder management, and ethics governance for sensitive populations
• Critical lens on AI and digital transformation through a social justice and equity framework.
• Knowledge of translating empirical findings into practitioner-facing instruments (checklists, toolkits), a high-impact form of research output beyond journal articles

Start date

2 November, 2026

End date

19 February, 2027

Location

QUT Y Block, Virtual

Additional information

Student researchers will receive hands-on training on research methodologies and qualitative data analysis.

Keywords

• GEDSI
• Inclusive process design
• Algorithmic bias

Contact

Dr Rehan Syed

0456036386

r.syed@qut.edu.au