Classification: principles, practice and the future

Published Date: 22 Apr 2024


It was hoped that the transition from sales tax to GST in 2000 would consign arid food classification disputes to distant memory. This has not happened. We continue to face many of the same old problems, albeit in new guises. Short of dramatic legislative change, there appears no obvious way around the deadweighting that classification visits on the system. Classification is basic to all human intellectual and other activities, however, not just to GST law and not just to food provisions. Common problems inhabit the whole landscape.

It has been said that “competent classification is essential to intellectual advance, and irresponsible classification leads to endless confusion”. This applies just as much to GST food as anything else. The judicial principles applied are easily enough stated. It is partly the intractable nature of the legislation itself, however, that makes compliance problematic. The law in this regard is a set menu, not à la carte. Courts tell us “it is a short practical question calling for a short practical answer”, but it rarely turns out that way at the margins. Multifactorial analysis based on “overall impression” or “common experience” applied to free-floating categories is a less than ideal way to make fiscal decisions. Reasonable minds may differ, there is no bright-line test, certainty suffers and the playing field tilts.

How did we get to this point and what might we do about it? It would be irresponsible not to ask if artificial intelligence (AI) technology advances may now provide a new and better way forward for tax classifications practice. Machine learning already provides classifiers across many disciplines, including in tax law. Algorithms are at the centre of this work, much of which was pioneered at the University of Toronto. Algorithmic classifier tools are used by the Canadian Revenue Authority, and a new one is in development for VAT food classifications in the UK. Less optimised versions are in limited GST use privately in Australia. In other words, a range of AI tools are already starting to do what this article set out to investigate.

A logical next step would be to review their wider application and, possibly, to see if and how they might be integrated into statutory decision-making processes. This raises a range of important rule-of-law and administrative law issues — such as transparency and accountability. A curious yet cautious approach is called for with the Robodebt Report pointing the way. An intrinsic issue is whether AI tools can provide “correct” results in practice, and what “correct” may mean in this context. There is room for some optimism, however, that classifier tools can provide more efficient ways to satisfy the statute. Comments in Simplot Australia Pty Ltd v FCT only confirm that we should “look to the future”, not only on GST food classifications, but across the fiscal classification spectrum generally for more modern and less onerous compliance.

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