INTHEBLACK May 2026 - Magazine - Page 37
AI is rapidly transforming
financial workflows, but biased
training data can embed unfair
or inaccurate outcomes into
critical decisions.
Mitigating algorithmic bias
requires continual oversight,
diverse data, transparent models
and strong, organisation-wide
governance structures.
Effective AI risk management
depends on human judgement,
governance and growing
AI literacy among finance
professionals.
Accounting for
algorithmic bias
Artificial intelligence promises efficiency and insight,
but also carries risk. Bias embedded in data and also
in the design of AI systems can distort outcomes if not
managed carefully.
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Words Rosalyn Page
ACCOUNTING AND FINANCIAL
services firms are adopting artificial
intelligence (AI) into their operations
to improve organisational efficiency
and accuracy. CPA Australia’s
Business Technology Report 2025 found
that 89 per cent of business used AI
in 2025. However, it also found that only
16 per cent of businesses use AI widely
across their organisations.
AI has many applications including data
analytics and insights, fraud detection, risk
assessment and financial reporting. However,
it does not come without risks — and one
of the major issues is algorithmic bias.
Research shows that the data used to train
AI models can contain biases or patterns
that affect results. Unfair or inequitable
outcomes can show up in risk assessments,
credit evaluations or financial policies.
Furthermore, the decision-making of AI
models is not always easily explainable,
raising questions about a lack of transparency
and trust in conclusions.
To address these risks, there is a growing
need for strategies to address algorithmic
bias via careful data management, model
oversight, governance and AI education.
The aim of such strategies is fairness,
accountability and compliance with
ethical standards.
HOW BIAS GETS IN
Bias enters AI systems in various ways,
such as skewed or unrepresentative data
and design, or configuration of the system.
As advanced pattern recognition systems,
AI tools can learn unwanted bias from the
input data, which can then be transferred
into the decision-making and output.
“AI is trained to identify patterns and then
exploit those patterns in new data to make
predictions,” says Dr Simon O’Callaghan,
head of technical AI governance at the
Gradient Institute. AI systems are often
trained on historical data that reflect human
subjectivity, he continues. For example,
which résumés look “good” or “bad”, or what
account activity looks suspicious, and those
judgements get encoded into the model
and reproduced in its outputs.
Fairness is not the same thing as
intheblack.cpaaustralia.com.au 37
