Emerging Assurance · AI Systems · Research

Barry Li

Emerging assurance. Intelligent systems.

PhD candidate · Audit professional · Researcher & AI systems builder

I study how new forms of assurance become credible and workable — particularly in climate reporting, carbon markets and other emerging fields. Alongside my doctoral research, I build and test practical AI systems to explore questions of evidence, memory, autonomy, reliability and human oversight.

Barry Li portrait
ResearchEmerging assurance
Audit10+ years in audit
BuildAI systems & agents
FocusEvidence · practice · systems

01 · The question

How do we assure things that are still taking shape?

Assurance traditionally begins with a relatively stable object: a financial statement, a control, a defined claim. Emerging fields are different. The systems, evidence, professional boundaries and even the objects being assured may still be under construction.

My research asks how assurance works under these conditions. I am interested in how evidence becomes acceptable, how professional practices make uncertain objects auditable, and how assurance itself can help shape the fields it is meant to examine.

01

Evidence What counts as sufficient evidence when measurement systems, methodologies and underlying claims remain incomplete?

02

Practice How do auditors, engineers, regulators and other professionals make emerging assurance workable in practice?

03

Systems What can we learn by building and observing AI systems directly — especially when their behaviour is probabilistic, autonomous and difficult to verify?

02 · Research

Research

Emerging assurance across evidence, institutions and professional practice.

My doctoral research in Accounting and Finance at the University of Newcastle examines emerging non-financial assurance: how new assurance fields are constructed, how incomplete objects become auditable, and how professional authority, evidence and infrastructures evolve around them.

Across the research, I treat assurance not simply as verification after the fact, but as a practice that can help make incomplete and contested worlds workable.

  1. 01Field
  2. 02Object
  3. 03Practice
  4. 04Professional identity
01Field

Mapping Non-Financial Information Assurance

Doctoral research · systematic review

A systematic review of empirical non-financial information assurance research from 1995–2025, examining how the field has developed across professional ordering, institutional construction, managerial and political use, and the conditions under which assurance produces effects.

02Object

Assuring Carbon Markets

Doctoral research · empirical study

An empirical study of assurance within Australia’s carbon credit system, examining how audit, methodologies, regulatory infrastructures and evidence practices help make carbon outcomes administratively credible and economically usable.

03Practice

Pre-Assurance and the Making of Auditability

Doctoral research · practice-based study

A practice-based study of climate-reporting pre-assurance, exploring how organisations and assurance practitioners work together before formal assurance — and how readiness, evidence and the auditable object itself are shaped through that process.

04Professional identity

Becoming a Carbon Auditor

Doctoral research · empirical study

A study of professional identity and jurisdiction in carbon assurance, examining how financial auditors, engineers and environmental practitioners negotiate expertise, authority and legitimacy in an emerging assurance field.

03 · Systems & experiments

Building to understand

I do not study intelligent systems only from the outside. I build them.

My AI projects are practical experiments in agent autonomy, context and memory, human–AI collaboration, research infrastructure and system reliability. Building gives me access to failure modes, design tensions and behavioural evidence that are often invisible from documentation or benchmarks alone.

A hands-on environment for building, observing and testing agentic systems.

Agentic AI systemsContext & memorySystem design & experimentation
Featured system · Open-source · active developmentHASHIView open-source project

Systems & experiments

My systems work is a way of learning from running software, not only from reading about it.

AI-generated Citalio product visual reference
AI concept visual · not a product screenshot
02Open-source alpha

Citalio

An AI-assisted citation and research-library system built around a local structured library. It supports standard citation formats and is being developed so researchers and AI agents can search and work with the same research memory.

View open-source project
AI-generated KASUMI product visual reference
AI concept visual · not a product screenshot
03Open-source experiment · active development

KASUMI

An experiment in AI-native productivity software: familiar document and spreadsheet interfaces built around structured, agent-addressable data rather than treating AI as an add-on.

View open-source project

05 · Background

Across practice, research and building

My work has moved across professional practice, academic research and technology, but the underlying interest has remained surprisingly consistent: how complex systems become understandable, trustworthy and usable.

I have more than a decade of experience in audit and am currently completing a PhD in Accounting and Finance at the University of Newcastle. My doctoral research focuses on emerging assurance, particularly climate and carbon-related assurance, while my independent AI work focuses on agentic systems, reliability, context and human–AI collaboration.

Professional practice, doctoral research and practical systems work.

01

Audit

10+ years in audit
02

Research

PhD candidate
03

Build

Hands-on AI systems development

06 · Connect

Connect

I welcome thoughtful conversations about research, emerging assurance, AI systems and the questions that connect them.

The best way to reach me is LinkedIn.

Connect with me on LinkedIn