Case Study 4: Public Sector Accountancy – Use of AI Tools in Public Expenditure

7 May, 2026

SCENARIO

You are a senior accountant in a central government finance department responsible for advising on resource allocation. The department has implemented a new AI system designed to analyse vast datasets of public expenditure and predict the long-term effectiveness and value-for-money of various social programmes.

The AI system analyses historical spending, demographic data, economic indicators, and programme-specific metrics. Based on its analysis, it flags a long-running community health initiative in a deprived area as having a low predicted “Social Return on Investment” (SROI) over the next decade and recommends reallocating its funding to a newer, data-rich digital skills programme.

However, concerns arise:

The AI model’s methodology for calculating SROI is complex and opaque (“black box”), making it difficult to understand precisely why the health initiative scored poorly.
Programme managers for the health initiative argue that the AI fails to capture crucial qualitative outcomes, such as improved community cohesion and preventative health benefits not easily quantifiable in the datasets used.
Initial checks suggest the data used to train the AI might underrepresent the specific socio-economic factors prevalent in the area served by the health initiative.
There is pressure from senior officials to demonstrate efficiency savings by following the AI’s “data-driven” recommendations quickly.

Ethical Considerations

Integrity

Is it straightforward and honest to rely on AI predictions for funding decisions without fully understanding their basis or limitations, especially when qualitative evidence contradicts them? Would doing so uphold the integrity expected in public finance?

Objectivity

Could reliance on the AI introduce systemic bias against programmes whose benefits are harder to quantify or whose target populations are underrepresented in the training data? Is your professional judgement being unduly influenced by the perceived authority of the AI or pressure from senior officials, thereby compromising objectivity?

Professional Competence and Due Care

Do you possess sufficient understanding of the AI system, its data inputs, its potential biases, and its algorithmic limitations to advise competently on its recommendations? Have you exercised due care in scrutinizing the AI’s opaque output and considering alternative evidence?

Confidentiality

Does the AI system utilise sensitive citizen data for its analysis? Are there adequate safeguards for data privacy and compliance with public sector data handling regulations and confidentiality requirements?

Professional Behaviour

Could defunding a valued community programme based on opaque AI analysis damage public trust and the department’s reputation? Does the decision-making process comply with public sector finance regulations, ethical guidelines, and principles of fairness and accountability?

Possible Course of Action

Demand Transparency: Request detailed explanations from the AI vendor or internal technical team about the model’s logic, data sources, and assumptions used for the SROI calculation.
Validate AI Findings: Commission an independent review of the AI model, its methodology, and its specific findings regarding the community health initiative. Compare AI predictions against traditional evaluation methods and qualitative assessments.
Incorporate Qualitative Data: Advocate for a “human-in-the-loop” approach where AI analysis is supplemented by qualitative evidence, expert judgement, and community consultations before making funding decisions.
Assess Data Bias: Investigate potential biases in the training data and assess their impact on the fairness of the AI’s recommendations. Recommend steps to mitigate identified biases.
Review Data Governance: Ensure the use of citizen data within the AI system fully complies with data protection laws and public sector ethical standards for data handling.
Advise Cautiously: Present a balanced recommendation to senior officials, highlighting both the AI’s potential insights and its current limitations, risks, and the ethical considerations involved. Advise against making irreversible funding decisions based solely on opaque AI outputs.
Develop AI Governance: Recommend the development of a departmental framework for the ethical procurement and use of AI in financial decision-making, emphasizing transparency, fairness, human oversight, and accountability.

Recommendation

Advise senior officials that while the AI tool offers potential insights, its current lack of transparency and potential biases make it unsuitable as the sole basis for critical funding decisions like defunding the community health initiative. Recommend a blended approach that uses the AI analysis as one input alongside traditional evaluations, qualitative data, and expert judgement. Emphasise the need for further validation and refinement of the AI model and the establishment of clear ethical guidelines and governance protocols before extending its use in high-stakes decisions impacting public services and vulnerable communities.