AI and ethics in tax administration

When explainability is absent, accountability vanishes.
 

Taxpayer trust cannot survive AI decisions that no one can explain or challenge. Participants agreed that a major ethical risk in tax AI systems lies not in their complexity but in their potential opacity. Even relatively straightforward models (those using 20 to 100 data points) can become “black boxes” if tax officials and taxpayers can't understand or audit the basis of decisions.

Examples like the Dutch childcare benefits scandal and the UK Post Office case illustrated the dangers: flawed systems, assumed to be completely reliable and shielded from scrutiny, created devastating real-world harms.

Participants called for mandatory transparency standards for AI models used in enforcement or assessment. Whether a decision is generated by a statistical model or a more advanced learning system, taxpayers must be able to ask, and receive a meaningful answer to, “why?”

Ignoring sensitive variables won’t eliminate bias.
 

Fairness by ‘not looking’ can be as dangerous as unfair targeting. There was strong concern that many tax administrations do not adequately monitor whether AI systems produce biased results. Models that omit sensitive variables like, gender or ethnicity, can still yield skewed outcomes — they may discover and rely on proxy indicators that correlate with protected characteristics.

Participants noted that in some cases, excluding such data actually increases the number of false positives for disadvantaged groups. For example, if male taxpayers are more likely to commit fraud, a model that doesn't know gender may overestimate risk for women.

Collecting and analyzing disaggregated compliance data (something institutions like the International Monetary Fund (IMF) have advocated) is essential to detecting and correcting systemic bias. But this remains patchy in practice.

AI changes the role of the tax administrator.
 

New technologies demand new skills and inclusive governance structures. AI tools are altering how tax professionals work. Risk detection, assessments and procedural decisions are increasingly assisted by AI, requiring staff to interpret and contextualize model outputs. This, participants noted, raises the bar for training and procedural clarity.

Tax administrators will need to understand not only tax law, but the structure, logic and limitations of the models they rely on. The blending of statistical reasoning with legal judgment creates a new kind of complexity — one many institutions are not yet ready for.

There were also concerns about who has access to these tools. In some administrations, AI development is confined to small internal groups, which may exacerbate issues of representation, particularly around gender and neurodiversity. Broader inclusion in governance and design processes was encouraged.

National AI strategies diverge — but tax data moves across borders.
 

Participants emphasized the growing divergence in regulatory approaches. The EU’s AI Act takes a risk-based stance, while the US has adopted more deregulatory postures in some domains. Meanwhile, countries like Japan, Australia and Chile are piloting lighter-touch or sector-specific approaches.

This patchwork increases the risk of regulatory arbitrage (particularly problematic in taxation, where digital data and corporate structures often span multiple jurisdictions). It also complicates the prospects for coordinated standards on transparency, fairness and accountability.

Participants agreed on the need for functional regulation governing specific uses of AI, such as risk scoring or case selection, rather than abstract definitions of AI itself. They called for internationally coordinated, principles-based frameworks that emphasize human oversight and due process.

Gradualism and simplicity may be the most responsible path forward.
 

Rather than ambitious end-to-end automation, participants encouraged tax administrations to focus on modest, high-impact use cases such as chatbots for taxpayer support or pre-filled returns using existing datasets. These applications offer clear efficiency gains and carry fewer ethical or legal risks.

Several speakers emphasized that AI should augment existing human-led processes, not bypass them. Hybrid systems that pair AI insights with human review and responsibility can build resilience while improving service delivery.

Caution was also urged against overreliance on AI-generated outputs for legal claims. Examples were cited where ‘hallucinated’ (e.g., factually incorrect or entirely fabricated) legal precedents appeared in taxpayer submissions, undermining both compliance and credibility.

Responsibility must be built in. Innovation without liability creates systems that evade correction.
 

Beyond the technology itself, participants stressed the importance of assigning clear responsibility within institutions. When decisions go wrong, there must be accountability — for design, oversight and outcomes.

Too often, organizational silos and unclear chains of command mean that no one takes responsibility when AI-driven decisions harm taxpayers. Without institutional structures that prioritize transparency and answerability, public trust will erode — regardless of technical performance. With them, performance is far easier to improve.

Participants pointed to the need for tax administrations to treat AI implementation as a governance issue, not just a technical upgrade. That includes embedding ethical review processes, maintaining documentation of model changes, and ensuring that all decisions are ultimately defensible by human staff. Well-designed AI enhancement could improve not only efficiency, but also organizational accountability, and perceptions of transparency and legitimacy by the taxpayers.

1. Joanna Bryson, Professor of Ethics and Technology, Hertie School, Berlin
2. David Hadwick, PhD researcher, DigiTax Centre of Excellence, University of Antwerp
3. Benita Mathew, Lecturer in AI and Fintech, University of Surrey
4. Dominic Mathon, Head of Tax & Treasury, RELX
5. Chris Morgan, Global Leader for the KPMG Responsible Tax Program, KPMG International
6. Tim Sarson, Partner, UK Head of Tax Policy, KPMG in the UK
7. Becky Holloway, Programme Director, Jericho
8. Neal Lawson, Partner, Jericho