INTHEBLACK May 2026 - Magazine - Page 39
“[Audits] are essential because they introduce structure,
transparency and accountability into systems that are
otherwise opaque.”
TIFFANY TAN FCPA, CPA AUSTRALIA
at once,” he notes. Attention needs to be
paid to model design and feature selection,
and there needs to be ongoing supervision and
monitoring of the system. This includes
curating and refreshing training data,
running fairness and performance tests
to assess models, and considering
independent validation.
“You need explainability layers,” Salampasis
says — mechanisms such as model
documentation, audit trails and independent
validation — which allow organisations
to interrogate and analyse systems and output.
AI risk assessment should also adopt
a holistic approach, addressing cybersecurity,
privacy, automation bias and legal compliance
in a manner proportionate to the system’s
use and risk profile, according to
Tiffany Tan FCPA, audit and assurance lead
at CPA Australia. Auditors, she says, can
provide safeguards for systems. Employing
risk assessment, controls evaluation and
professional scepticism, auditors possess
foundational expertise that can be applied
to algorithms.
Audits can provide an independent
assessment of governance and control
arrangements around how models are used,
how risks are mitigated, and whether outputs
are overseen for explainability, fairness and
reliability, Tan continues. “In doing so, audits
support transparency and accountability
without substituting for management
responsibility or technical model validation.”
GOVERNANCE IS ESSENTIAL
Strong governance is needed to manage
the risks associated with AI. It needs to
be the foundation of AI workflows — not
an optional add-on.
Algorithmic bias can also intersect with
anti-discrimination frameworks and human
rights considerations. Decisions involving
protected attributes could amount to
unlawful discrimination, according to an
Australian Human Rights Commission
RECOMMENDATIONS FOR AUDITORS
As organisations accelerate their adoption of AI tools,
the accounting and finance profession’s existing strengths
place auditors at the forefront of responsible oversight.
“Auditors already possess foundational expertise in
risk assessment, controls evaluation, independence and
professional skepticism that positions them well to provide
trust in this evolving area,” says Tiffany Tan FCPA, audit
and assurance lead at CPA Australia. She has five key
recommendations for auditors when assessing AI use:
1. Understand how clients are using AI across financial
reporting processes and internal controls.
2. Ask tough questions: What data is being used? How
reliable are outputs? What controls exist? Who reviews
AI-assisted decisions?
3. Be prepared to assess how AI affects risk of material
misstatement.
4. Develop skills in evaluating AI-related evidence, including
when AI-generated outputs are not sufficient.
5. Prepare for emerging AI assurance services, which will
become increasingly relevant.
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