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27 June 2025
6 min read
Admin

Ethics and AI: Managing Risks, Bias and Data Security in Accounting

As AI becomes increasingly integrated into accounting workflows, firms must develop robust frameworks to address privacy concerns

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The rapid adoption of artificial intelligence in accounting practices brings transformative benefits, but it also introduces significant ethical challenges. For Australian accountants, understanding and managing these risks is essential to maintaining professional standards, protecting client interests, and ensuring the integrity of financial information. As AI becomes increasingly integrated into accounting workflows, firms must develop robust frameworks to address privacy concerns, mitigate bias, and maintain the profession's ethical foundations.

Understanding the Ethical Risks of AI in Accounting

Data Privacy and Confidentiality

KPMG highlights that a key concern is data privacy – client financial data fed into AI systems (especially cloud or third-party platforms) must be safeguarded and kept confidential. This concern is particularly acute in accounting, where practitioners handle sensitive financial information that could cause significant harm if exposed or misused.


Privacy risks include:

  • Unauthorised data exposure through insecure AI platforms
  • Data retention by third-party AI providers beyond intended use
  • Cross-client contamination where AI systems might inadvertently use one client's data to inform analysis for another
  • Jurisdictional challenges when data is processed in different countries with varying privacy laws

Algorithmic Bias

AI systems learn from historical data, which can embed and perpetuate existing biases. In accounting contexts, this might manifest as:

  • Credit risk assessments that unfairly disadvantage certain demographics
  • Audit sampling that systematically overlooks certain types of transactions
  • Performance metrics that reflect historical inequities rather than current capabilities
  • Financial forecasts that perpetuate past patterns without accounting for changing circumstances

KPMG warns that algorithms trained on biased data could produce skewed analyses, potentially leading to flawed business decisions or unfair treatment of stakeholders.

AI Hallucinations and Accuracy

KPMG notes that generative AI may sometimes "hallucinate" incorrect facts. In accounting, where precision is paramount, these hallucinations pose serious risks:

  • Fabricated figures in financial analysis
  • Incorrect regulatory references in compliance work
  • Misrepresented accounting standards in technical advice
  • False citations in audit documentation

These errors can be particularly dangerous because AI often presents information with apparent confidence, making hallucinations difficult to detect without careful verification.

Practical Policies for Risk Management

Restricting Use of Public AI Tools

CPA Australia's INTHEBLACK reports that firms are instituting strict policies, for example prohibiting input of sensitive client information into public AI tools like ChatGPT. This policy reflects the recognition that public AI platforms:

  • May retain and use input data for model training
  • Lack the security controls required for confidential information
  • Cannot guarantee data isolation between users
  • May not comply with professional confidentiality obligations

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Implementing Data Handling Protocols

Firms are developing comprehensive data handling protocols that include:

  • Classification systems for data sensitivity levels
  • Approved AI platforms that meet security standards
  • Access controls limiting who can use AI tools with client data
  • Audit trails tracking what data has been processed through AI systems
  • Data anonymisation procedures before AI processing where possible

Validation and Review Processes

To address accuracy concerns, firms are implementing:

  • Mandatory human review of all AI-generated outputs
  • Source verification for any facts or figures produced by AI
  • Cross-checking procedures using independent methods
  • Documentation requirements for AI-assisted work
  • Regular accuracy audits of AI system performance

AI Governance Frameworks

KPMG has developed AI governance frameworks to oversee how AI is implemented – focusing on transparency, accountability, and quality control. These frameworks typically encompass:

Core Governance Principles

1. Transparency: Clear documentation of how AI systems make decisions
2. Accountability: Defined responsibilities for AI outcomes
3. Fairness: Processes to identify and mitigate bias
4. Reliability: Consistent and predictable AI performance
5. Privacy: Protection of personal and confidential information
6. Security: Safeguards against unauthorised access or manipulation

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Governance Structure Elements

  • AI Ethics Committee: Senior leadership group overseeing AI use
  • Risk Assessment Protocols: Regular evaluation of AI-related risks
  • Training Programs: Ensuring staff understand AI capabilities and limitations
  • Vendor Management: Due diligence on third-party AI providers
  • Incident Response Plans: Procedures for addressing AI failures or breaches

Implementation Controls

Effective governance requires practical controls including:

  • Use case approval processes before deploying AI for new purposes
  • Regular model audits to detect drift or degradation
  • Performance monitoring against defined metrics
  • Stakeholder communication about AI use in services
  • Continuous improvement based on lessons learned

Professional Standards and Regulatory Guidance

IFAC and IESBA are updating ethical guidelines to address AI use in accounting. Key principles emerging from professional bodies include:

Maintaining Professional Competence

Accountants must:

  • Understand the AI tools they use
  • Recognise limitations and potential errors
  • Maintain skills to validate AI outputs
  • Stay current with evolving AI capabilities

Preserving Professional Scepticism

To address bias and accuracy issues, accountants need to maintain professional scepticism and validate AI-generated results. This includes:

  • Questioning AI recommendations
  • Seeking corroborating evidence
  • Understanding AI reasoning processes
  • Identifying potential conflicts of interest in AI design

Ensuring Human Accountability

Regulatory bodies and accounting associations reinforce that responsibility for accuracy and integrity ultimately remains with the human professional, regardless of AI assistance. This means:

  • Accountants cannot delegate professional judgment to AI
  • Final decisions must involve human oversight
  • Errors remain the responsibility of the practitioner
  • Professional liability extends to AI-assisted work

Best Practices for Ethical AI Implementation

Start with Strong Foundations

1. Develop clear AI policies before implementation
2. Train all staff on ethical AI use
3. Choose vendors carefully based on security and ethics
4. Start small with low-risk applications
5. Monitor continuously for emerging issues

Build Ethical Considerations into Processes

  • Include ethics reviews in AI project planning
  • Document decision-making processes
  • Create feedback mechanisms for concerns
  • Regularly review and update policies
  • Share learnings across the profession

Foster a Culture of Responsible AI Use

  • Lead by example from senior leadership
  • Reward ethical decision-making
  • Encourage reporting of concerns
  • Celebrate successful ethical implementations
  • Learn from mistakes without blame

Looking Forward: The Evolution of AI Ethics

As AI technology advances, ethical considerations will continue to evolve. Emerging areas of focus include:

  • Explainable AI that can articulate its reasoning
  • Federated learning that protects data privacy
  • Differential privacy techniques for analytics
  • Blockchain integration for audit trails
  • Regulatory technology for compliance monitoring

Conclusion

The integration of AI into accounting practices offers tremendous opportunities for efficiency and insight, but it must be balanced with robust ethical safeguards. By implementing comprehensive governance frameworks, maintaining professional scepticism, and prioritising client data protection, Australian accountants can harness AI's benefits while upholding the profession's ethical standards.

As KPMG's guidance emphasises, successful AI adoption requires more than technical implementation – it demands a commitment to transparency, accountability, and continuous vigilance. The firms that get this balance right will not only avoid ethical pitfalls but also build stronger trust with clients and stakeholders.

The message is clear: AI is a powerful tool, but professional judgment, ethical standards, and human accountability remain the cornerstone of the accounting profession. By embracing both innovation and responsibility, Australian accountants can lead the way in demonstrating how AI can enhance rather than compromise professional integrity.

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Written by Admin
Last updated: 27 June 2025

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