AI Ethics in Accounting: Can Algorithms Be Biased in Financial Decisions?

Artificial Intelligence (AI) is rapidly transforming the landscape of accounting and finance, powering decisions from audits to credit ratings and fraud detection with remarkable speed and scale. Yet beneath its sleek algorithms lies a critical question that demands deeper reflection: Can AI itself be biased in financial decisions? And if so, what risks does this pose for the integrity and fairness of the financial ecosystem?

1. AI Ethics in Accounting: Can Algorithms Be Biased in Financial Decisions?
Artificial Intelligence (AI) is rapidly transforming the landscape of accounting and finance,
powering decisions from audits to credit ratings and fraud detection with remarkable speed
and scale. Yet beneath its sleek algorithms lies a critical question that demands deeper
reflection: Can AI itself be biased in financial decisions? And if so, what risks does this pose for
the integrity and fairness of the financial ecosystem?
When Algorithms Inherit Human Bias
AI in accounting doesn’t operate in a vacuum. These systems learn and make decisions based
on historical financial data and prior human judgments. However, past data often carries
hidden biases reflecting societal inequalities or flawed decision-making patterns. Without
careful checks, AI can unintentionally amplify these biases—not eliminate them.
For example, in 2019, Apple and Goldman Sachs came under scrutiny for allegedly issuing
credit cards with significantly lower limits to women compared to men, despite comparable
financial profiles. Although Goldman Sachs denied intentional bias, this incident shines a
spotlight on how AI-driven credit decisions can inadvertently reflect discriminatory patterns
ingrained in training data.
Ethical Risks in AI-Powered Financial Decisions
Several core risks arise from deploying AI in finance, especially in auditing, credit scoring, and
fraud detection:
● Discriminatory Credit Outcomes: Some machine learning credit models have been found to
charge higher interest rates or deny loans disproportionately to minority groups, perpetuating
systemic inequities even when controlling for creditworthiness.
● Opacity and “Black Box” Decisions: Complex AI models, such as neural networks, often
provide little transparency on how specific outputs are derived. This “black box” phenomenon
can prevent affected parties—and sometimes even auditors—from fully understanding or
challenging AI-driven outcomes.

2. ● Data Quality Issues: AI is heavily dependent on the quality and representativeness of input
data. Historical financial data may exclude or misrepresent certain demographics, leading to
skewed or unfair results.
● Over-reliance on Algorithms: The emerging practice of deferring too much authority to AI-
generated decisions—sometimes dubbed “automation bias”—raises the risk of complacency,
where human oversight weakens and errors go undetected.
● Accountability Gaps: Multiple parties—technology vendors, data providers, financial
institutions—contribute to these AI systems, complicating who is responsible when things go
wrong, especially with biased or wrong decisions.
Fraud Detection and AI Bias: A Double-edged Sword
AI plays a vital role in detecting financial fraud by analyzing transaction patterns to flag
anomalies. Yet, biased training data or flawed model design can falsely associate certain
names, locations, or behaviours with higher fraud risk, unfairly targeting specific population
groups. Such biases do not just damage reputations; they undermine trust in financial
oversight and enable criminals to exploit blind spots where AI checks are less sensitive.
Toward Ethical AI in Accounting: Principles and Best Practices
Recognizing these risks, the financial sector is increasingly focusing on embedding ethics into
AI development and deployment:
● Transparency: Making AI decision-making processes explainable helps stakeholders
understand how outcomes are reached and detect bias early.
● Inclusive and Representative Data: Data sets should capture diverse populations and
scenarios to minimize bias and promote equitable treatment.
● Continuous Monitoring and Auditing: Regular fairness audits and updates address evolving
biases or errors, ensuring AI systems remain aligned with ethical standards.
● Accountability Mechanisms: Clear governance structures define who is responsible for AI
decisions and failures, preventing diffused liability and reinforcing ethical use.
● Cross-functional Collaboration: Combining expertise from AI developers, ethicists, auditors,
and business leaders fosters balanced system design sensitive to ethical and practical
concerns.
The Human Touch Matters
Ultimately, AI in accounting should augment rather than replace human judgment. Ethical use
demands vigilance to detect when algorithms may reinforce biases or overlook novel risks,
especially in areas like auditing where nuances are critical. As financial systems become more
automated, preserving human oversight and embedding ethics in AI design are the linchpins
to trustworthy financial decision-making.

3. AI presents extraordinary opportunities to revolutionize accounting, offering precision and
efficiency beyond traditional methods. But as history and examples remind us, ethical
vigilance is essential to prevent algorithms from perpetuating unfair financial decisions hidden
behind a veneer of objectivity. As professionals, educators, and learners, understanding these
challenges invites us all to engage in shaping AI’s role in finance with responsibility and
fairness at the core.
What caused the Apple Card bias controversy and what lessons remain
The Apple Card bias controversy was caused by the credit-lending algorithm, managed by
Goldman Sachs, allegedly giving significantly lower credit limits to women compared to men,
even when the women had equal or better financial profiles. This issue gained widespread
attention when David Heinemeier Hansson, the creator of the Ruby on Rails software, publicly
reported on Twitter that his wife received a credit limit 20 times lower than his, despite filing
joint tax returns and sharing financial assets. Apple co-founder Steve Wozniak also shared a
similar experience, confirming the issue was systemic rather than isolated. The controversy
highlighted that the algorithm was a "black box"—neither Apple nor Goldman Sachs customer
service representatives could explain or override its decisions, which deepened the frustration
and mistrust.
The root of the problem lay in the opaque nature of the AI-driven credit decision process,
which likely used historical financial data and credit risk parameters that did not adequately
account for factors like joint ownership of assets or community property laws. Some experts
suggested that women were disadvantaged due to factors baked into the algorithm, such as
using individual income rather than household income, in a system where women generally
earn less than men on average. Additionally, the lack of transparency in how the credit limits
were assigned meant there was no clear way to audit or detect algorithmic bias during the
decision-making process.
Following public outcry and regulatory attention, the New York Department of Financial
Services launched an investigation into Goldman Sachs. The bank acknowledged potential
issues and pledged to review and adjust its credit decisioning processes to prevent unintended
bias.
Lessons from the Apple Card Controversy:
1. Transparency is Crucial: Black box algorithms that impact financial decisions without
explainability create serious trust issues and regulatory risks. Firms must develop AI systems
whose logic can be audited and explained to affected customers.
2. Bias Can Be Hidden in Data and Models: Even without explicit discrimination, algorithms
can produce biased outcomes if trained on historical data that reflects societal inequities or
uses flawed assumptions. Careful scrutiny of data inputs and model parameters is necessary.
3. Human Oversight Matters: Over-reliance on automated AI decisions without clear
escalation paths or review mechanisms can deepen unfair outcomes and customer
frustration. Maintaining human intervention points is vital.

4. 4. Regulation and Accountability Are Increasing: Financial regulators are paying close attention
to AI in lending and credit decisioning. Institutions have legal and reputational incentives to
proactively detect and mitigate AI bias.
5. Collaboration Across Fields is Needed: Addressing AI ethics requires finance, technology,
legal, and ethics experts working together to create fair, transparent, and socially responsible
financial AI systems.
The Apple Card case remains a landmark example underscoring that while AI can streamline
lending decisions, the design, transparency, and oversight of these algorithms must be
handled with utmost care to prevent perpetuating discrimination and to maintain trust in
financial services.