Strategic Business Leader · Machine learning, AI and robotics

Ethical, Governance and Professional Issues of AI

Updated 11 October 2026 · Fact-checked

AI ethics covers the moral, governance and professional risks of using automated decision systems: bias, lack of transparency, privacy breaches, unclear accountability and job loss. In SBL you identify the issue in the scenario, apply ethical principles and governance controls, and recommend practical actions as a professional accountant.

Understand Ethical, Governance and Professional Issues of AI

Artificial intelligence (AI) systems learn from data and make or support decisions. That brings benefits such as speed and consistency. It also brings ethical risks, because the system is only as good as its data, design and oversight.

The main issues to know are:
- Bias and fairness: If training data reflects past prejudice, the AI repeats it. Examples are unfair loan, hiring or pricing decisions.
- Transparency (the black box problem): Some models cannot explain why they reached a result. Customers, regulators and auditors cannot challenge what they cannot understand.
- Accountability: A machine cannot be held responsible. The board and management remain accountable for AI outcomes, and cannot blame the algorithm.
- Data privacy: AI needs large amounts of data. Collecting, sharing or using personal data beyond its original purpose breaches trust and may breach data protection law.
- Jobs and society: Automation removes routine roles, may harm morale and community, and creates a duty to treat staff fairly through retraining and honest communication.

Governance is how the organisation controls these risks. Good practice includes board-level ownership of AI, an AI policy and ethical code, testing for bias before and after launch, human review of significant decisions, clear data governance, and monitoring by internal audit and the audit committee. Stakeholders such as customers, employees, regulators and shareholders all have different interests, so weigh them.

The accountant's role changes. Routine processing and reconciliations are automated. Value moves to judgement, interpretation, advice and challenge. Accountants must keep professional scepticism: do not accept AI output without questioning the data, assumptions and logic. They must also follow the ACCA Code of Ethics: integrity, objectivity, professional competence and due care, confidentiality and professional behaviour. Over-reliance on a tool you do not understand threatens competence and due care. Using client data in an AI tool can threaten confidentiality. Pressure to accept a favourable output can threaten objectivity.

In the exam, always link the issue to the scenario facts and to the public interest. Show professional skills: analysis, scepticism, commercial acumen, and clear, balanced communication.

Key rules to remember

ACCA fundamental principles: Integrity, Objectivity, Professional competence and due care, Confidentiality, Professional behaviour; Use these to frame AI dilemmas for accountants. Name the principle under threat and explain why.
Ethical threat types: Self-interest, Self-review, Advocacy, Familiarity, Intimidation; Over-reliance on an AI tool you built or sold can create self-review or self-interest threats. Pick only those that fit the facts.
Response to a threat: Identify threat → Evaluate significance → Apply safeguards → If not reduced to an acceptable level, decline or withdraw; Gives a safe structure for any ethics requirement.
AI governance checklist: Ownership, Policy, Data quality, Bias testing, Human oversight, Transparency, Monitoring; A memory list for recommendations. Select the items that match the scenario.

How to solve Ethical, Governance and Professional Issues of AI questions

Use this method for any SBL requirement on AI ethics, governance or the accountant's role.

1Read the requirement and note the verb (discuss, evaluate, advise, recommend) and who you are writing as.
2Underline the scenario facts: what AI does, whose data it uses, who is affected, and any pressure or deadline.
3Identify the specific issues: bias, transparency, accountability, privacy, job impact or scepticism.
4Link each issue to a stakeholder or an ethical principle, and explain the consequence for this organisation.
5Weigh both sides. Say what the benefits are and why the risks matter, so your answer is balanced.
6Recommend practical actions: governance controls, human oversight, staff retraining, policy or escalation.
7Write in the required format (report, memo, email) with a clear opening and conclusion to earn professional skills marks.
8Check that every point refers to the scenario and that you have answered the exact requirement.

Quickest way: Issue, impact, action

When to use it: Use when time is short and the requirement asks you to discuss or advise on AI ethical concerns.

List the issues in the margin using the prompts: bias, black box, accountability, privacy, jobs, scepticism.
For each issue write one sentence on the scenario fact and one on the consequence.
Add one specific action per issue.
Finish with a short conclusion that gives a clear recommendation, which scores professional skills marks.

Common mistakes in Ethical, Governance and Professional Issues of AI

Writing a generic essay on AI with no scenario facts.
Students recall notes and skip the case details.
Fix: Quote or paraphrase a scenario fact in every paragraph and say what it means for this company.
Saying the AI is responsible for a wrong decision.
The system makes the decision, so it feels like the cause.
Fix: State that the board and management stay accountable and must keep human oversight and clear lines of responsibility.
Listing the five fundamental principles without applying them.
Students treat the code as a memory test.
Fix: Choose the principle at risk, explain why, and give a safeguard tied to the facts.
Only discussing risks and ignoring benefits.
Ethics questions seem to ask for negatives.
Fix: Give a balanced view of efficiency and accuracy gains versus risks, then recommend a way to capture benefits safely.
Ignoring the accountant's personal role and scepticism.
Students focus on the company rather than the professional.
Fix: Add how you would challenge AI output, check data and assumptions, document your judgement and escalate concerns.
Treating job losses only as a cost saving.
A finance viewpoint dominates.
Fix: Cover employee impact, morale, reputation and retraining, as well as savings, and suggest fair communication and reskilling.

Worked examples

Example 1

A bank uses an AI model to approve small business loans. Rejection rates are far higher for businesses in one region, and managers cannot explain individual decisions. You are the finance director's adviser. Discuss the ethical and governance issues and recommend actions.

Show the solution

Issue 1, bias: the regional gap suggests the training data or variables may encode past prejudice. This risks unfair treatment, reputational damage and regulatory action.
Issue 2, transparency: managers cannot explain decisions. Customers cannot challenge rejections and the bank cannot show it is acting fairly.
Issue 3, accountability: the board is responsible for outcomes. It cannot hand responsibility to the model.
Stakeholders: applicants lose access to finance, regulators may investigate, and shareholders face reputational and legal risk.
Action: test the model for bias across regions and correct the data or variables.
Action: require human review of rejections, especially borderline cases, and an appeals process.
Action: set up board-level ownership, an AI policy, and regular monitoring by internal audit reporting to the audit committee.
Conclusion: keep the AI for its speed, but only with explainability, oversight and monitoring in place.

Answer: The model raises bias, transparency and accountability concerns. The bank should test and correct for bias, add human review and appeals, assign board ownership, and monitor through internal audit and the audit committee.

Example 2

You are a management accountant. Your company has bought AI software that produces cash flow forecasts. Your finance director asks you to use the output in a lender presentation without review, because the figures look favourable. Discuss the professional and ethical issues for you.

Show the solution

Principle at risk, objectivity: pressure to accept favourable numbers is an intimidation or self-interest threat.
Principle at risk, professional competence and due care: using output you have not checked or do not understand falls short of due care.
Principle at risk, integrity: giving lenders figures you cannot support could mislead them.
Professional scepticism: question the data inputs, assumptions, model logic and whether results are reasonable compared with past performance.
Safeguards: perform reasonableness checks, run sensitivity scenarios, document your work and ask the supplier or IT for model details.
Escalation: explain the concerns to the finance director, and if unresolved raise them with the audit committee or follow the company's internal procedures. Consider seeking ethical advice from ACCA.
Public interest: lenders rely on the figures, so misleading them harms third parties.

Answer: Do not present the forecast unreviewed. Apply scepticism, check and document the forecast, and escalate if pressure continues, to protect objectivity, due care and integrity.

Exam tips

Always name the stakeholder harmed and the specific consequence. Marks go for application, not definitions.
When the requirement says 'as the accountant', show personal ethics and scepticism as well as company governance.
Give balanced views and a clear recommendation. Professional skills marks reward judgement and communication.
Use the fundamental principles only where the facts justify them, and say why.

Practice questions from Machine learning, AI and robotics

Ethical, Governance and Professional Issues of AI in other exams

The same ground in other exams, if you are preparing for more than one or want another angle on it.

Ethical, Governance and Professional Issues of AI: frequently asked questions

What are the main ethical issues of AI for SBL?

The main ones are bias, lack of transparency, unclear accountability, data privacy and the effect on jobs. Always tie each one to the scenario and its stakeholders.

Who is accountable when AI makes a wrong decision?

The organisation's board and management remain accountable. AI is a tool, so you should recommend oversight, clear responsibility and human review of important decisions.

How does AI change the accountant's role?

Routine processing is automated, so the accountant spends more time on analysis, judgement, advice and challenge. Professional scepticism and competence in understanding the tools become more important.

How do I earn professional skills marks on an AI ethics question?

Analyse the scenario facts, question the information with scepticism, show commercial awareness and communicate in the required format. End with a clear, balanced recommendation.