INTHEBLACK May 2026 - Magazine - Page 40
F E AT U R E
report, which recommends proactively
identifying human rights risks and assessing
impacts before and during deployment.
For organisations, this includes clear AI
policies that define responsible and ethical
use, with clear roles and responsibilities for
oversight, Tan says. Staff training is also
needed to ensure there is an understanding
of the limitations of the technology.
Salampasis argues that questions of bias
cannot be separated from how models are
designed and governed. He stresses the
importance of transparency, accountability
and alignment with professional standards,
particularly in high-risk financial contexts.
Without transparency and accountability,
it becomes difficult to challenge or verify
AI-driven decisions and identify who is
responsible for which outcomes.
Governance must include data sourcing
to understand the origins of data and
any potential issues. However, as with
algorithmic bias mitigation, AI governance
cannot sit solely with engineers or
accountants, Salampasis continues.
There is a need for interdisciplinary teams
that facilitate financial, legal and technical
collaboration and oversight, particularly
in high-risk financial contexts.
It must also extend beyond internal
systems to include vendor due diligence
to understand how third-party AI tools
have been trained and developed.
READ
an article on
accountant’s
crucial role in
AI governance
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episode on
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40 INTHEBLACK May 2026
REGULATION IS CATCHING UP
Increasingly, AI governance is also being
defined at the regulatory level.
Best practice is to have documented
governance frameworks. Organisations can
look to trusted standards for guidance, such
as the US National Institute of Standards
and Technology’s AI risk management
framework playbook, the International
Organisation for Standardisation and
International Electrotechnical Commission’s
AI management standards, or the Committee
of Sponsoring Organisations’ enterprise risk
management framework.
Salampasis points to the European Union’s
AI Act as a structured, risk-based framework
for AI, with heightened governance
obligations for high-risk systems. This includes
financial services, particularly areas such as
credit scoring.
Australia does not have an economy-wide
AI regulatory regime equivalent to the
EU AI Act, and in fact has signalled it does
not intend to introduce one. The Australian
Government argues that existing laws such
as the Privacy Act (1988), Australian
Consumer Law and The Corporations Act
(2001) are sufficient to manage AI-related
risks. Nonetheless, it has stated it will
intervene where regulatory gaps emerge.
The National AI Plan and Guidance for
AI Adoption support this approach by
providing practical risk management
measures and guidance on ethical principles
for AI adoption.
For auditors in particular, governance
must extend to how AI is used within audit
engagements and align with International
Standard on Quality Management
requirements. “There needs to be processes
for documenting human-in-the-loop
reviews so that human oversight,
professional scepticism and judgement
remain central,” Tan says. “Good governance
does not eliminate risk, but it ensures
AI risk is measured, managed, explainable
and auditable.”
DEVELOP AI LITERACY
AI risk management is becoming a core
professional responsibility, not solely
a technical safeguard. It has heightened
the need for AI literacy and critical
evaluation skills among accounting and
finance professionals.
“The profession must treat AI as both an
opportunity and a capability shift,” Tan says.
There is an increasing need to develop
a sufficient understanding of AI to ask
informed questions, recognise risk signals
and interpret outputs critically. In practice,
this means that professionals must treat
AI as a powerful assistant — not an
authority — to ensure final judgement
always remains with humans, and build
training into professional development.
This includes training on prompting and
interrogation of AI tools, applying
industry-specific risk lenses when using AI,
and embedding ethics and bias awareness
into AI-related work.
“It should augment, not replace, professional
expertise,” Tan says. “Even the most advanced
AI cannot replace the judgement of a trained
accountant or auditor.” ■
