Every day, multinational firms process thousands of transactions, each of which must adhere to tax regulations that vary by jurisdiction and are often nuanced. The determination of product and service tax codes, such as HSN or SAC is a major use case in Tax compliance. An accurate determination of such codes is imperative to avoid any tax penalties. This paper proposes a domain-adaptive small language model (SLM) with an encoder-decoder architecture for the enhanced prediction of product and service tax codes. In this approach, we address the problem of predicting hierarchical tax code sequences using unstructured product and services data. We employ an SLM based upon encoder-decoder architecture as this enables sequential generation of tax codes to capture the hierarchical dependencies present within the tax codes. Our experiments demonstrate that encoder-decoder SLMs can be successfully applied to the sequential prediction of structured tax codes, a domain that remains comparatively unexplored in current NLP research. In this paper, we demonstrate the superior performance of the domain-adaptive encoder-decoder SLMs over flat classifiers when applied to the Harmonized System of Nomenclature (HSN), and achieve superior results compared to decoder-only and encoder-only architectures for structured sequence generation tasks. This approach can also be scaled to other government-mandated tax commodity codes, such as United Nations Standard Products and Services Codes (UNSPSC), or Brazil's Nomenclatura Comum do Mercosul (NCM).

Every year, an army of hackers takes aim at the tax code. The tax code is not computer code, but it is a series of rules--supposedly deterministic algorithms--that take data about your income and determine the amount of money you owe. This code has vulnerabilities, more commonly known as loopholes. It has exploits; those are tax avoidance strategies. There is an entire industry of black-hat hackers who exploit vulnerabilities in the tax code: We call them accountants and tax attorneys.

Artificial intelligence--AI--is an information technology. And it is already deeply embedded into our social fabric, both in ways we understand and in ways we don't. It will hack our society to a degree and effect unlike anything that's come before. I mean this in two very different ways. One, AI systems will be used to hack us. And two, AI systems will themselves become hackers: finding vulnerabilities in all sorts of social, economic, and political systems, and then exploiting them at an unprecedented speed, scale, and scope. We risk a future of AI systems hacking other AI systems, with humans being little more than collateral damage. Okay, maybe it's a bit of hyperbole, but none of this requires far-future science-fiction technology. I'm not postulating any "singularity," where the AI-learning feedback loop becomes so fast that it outstrips human understanding. My scenarios don't require evil intent on the part of anyone. We don't need malicious AI systems like Skynet (Terminator) or the Agents (Matrix). Some of the hacks I will discuss don't even require major research breakthroughs. They'll improve as AI techniques get more sophisticated, but we can see hints of them in operation today. This hacking will come naturally, as AIs become more advanced at learning, understanding, and problem-solving. In this essay, I will talk about the implications of AI hackers. First, I will generalize "hacking" to include economic, social, and political systems--and also our brains. Next, I will describe how AI systems will be used to hack us. Then, I will explain how AIs will hack the economic, social, and political systems that comprise society. Finally, I will discuss the implications of a world of AI hackers, and point towards possible defenses. It's not all as bleak as it might sound. Caper movies are filled with hacks. Hacks are clever, but not the same as innovations. Systems tend to be optimized for specific outcomes. Hacking is the pursuit of another outcome, often at the expense of the original optimization Systems tend be rigid. Systems limit what we can do and invariably, some of us want to do something else. But enough of us are. Hacking is normally thought of something you can do to computers. But hacks can be perpetrated on any system of rules--including the tax code. But you can still think of it as "code" in the computer sense of the term. It's a series of algorithms that takes an input--financial information for the year--and produces an output: the amount of tax owed. It's deterministic, or at least it's supposed to be.

For one of the most distinguished critics of automation, MIT economist Daron Acemoglu has been, ironically, cranking out research on the subject lately like he's a machine. He and his co-author Pascual Restrepo have produced so many studies on the subject that he couldn't tell us how many they've done. "I've lost count," he says. Their conveyer belt of research has been spitting out some startling facts. They find, for instance, that each new industrial robot killed, on average, 3.3 jobs in America between 1993 and 2007.

For the past couple of years, renowned technologist and researcher Bruce Schneier has been researching how societal systems can be hacked, specifically the rules of financial markets, laws, and the tax code. That led him to his latest examination of the potential unintended consequences of artificial intelligence on society: how AI systems themselves, which he refers to as "AIs," could evolve such that they automatically - and inadvertently - actually abuse societal systems. "It's AIs as the hacker," he says, rather than hackers hacking AI systems. Schneier will discuss his AI hacker research in a keynote address on Monday at the 2021 RSA Conference, which, due to the pandemic, is being held online rather than in person in San Francisco. The AI topic is based on a recent essay he wrote for the Cyber Project and Council for the Responsible Use of AI at the Belfer Center for Science and International Affairs at Harvard Kennedy School.

If you don't have enough to worry about already, consider a world where AIs are hackers. Hacking is as old as humanity. We are creative problem solvers. We exploit loopholes, manipulate systems, and strive for more influence, power, and wealth. To date, hacking has exclusively been a human activity.

Register for free or grab a discounted VIP pass today. Tax compliance platform Blue Dot (previously VatBox) today announced it has raised $32 million, bringing its total raised to over $96 million. The firm says it will put the funds toward product R&D and expanding the size of its globlal workforce. The tax compliance burden for enterprises can be significant. In 2019, half of companies responding to an EY Americas survey indicated that their biggest compliance challenge would be staying current on legislative and regulatory developments.

The debate over automation has been overshadowed by more immediate economic problems created by the coronavirus crisis. But when things return to some semblance of normality, it's sure to crop up again and may well play a role in how a recovery takes shape. The basic question is whether automation is good or bad for average workers. The latest salvo against the robots comes from economists Daron Acemoglu, Andrea Manera, and Pascual Restrepo. In a recent National Bureau of Economic Research paper entitled "Does the US Tax Code Favor Automation?," they argue that taxes are higher on labor than on capital equipment, causing companies to invest too much in machines and not enough in manpower.

On this week's episode of my podcast, I Have to Ask, I spoke to Annie Lowrey, a contributing editor at the Atlantic and the author of the new book Give People Money: How a Universal Basic Income Would End Poverty, Revolutionize Work, and Remake the World. It's about universal basic income--the idea that the government would give all its citizens checks every month. Versions of this proposal have caught on with people on the left as well as tech leaders in Silicon Valley and even some hardcore libertarians. Lowrey has written for many years now about economics, but Give People Money is both a reported work--she travels to Kenya, South Korea, and India to view their economic experiments--and a policy brief on what she believes can help alleviate some of the social and political discontent that has arisen from economic change and dislocation. Below is an edited excerpt from the show. In it, we discuss the benefits and drawbacks of UBI, whether or not we should be skeptical that so many Silicon Valley titans have embraced the idea, and how to make the safety net less vulnerable to political attacks.

It's been just over 30 years since the last major overall of the U.S. tax code. In that time the world has been transformed - the Soviet Union collapsed, the Berlin Wall fell, dot coms boomed and busted, terrorism struck and launched the U.S. into the longest war in its history. A financial crisis shook the country and the world to its knees, and the rise of big data, artificial intelligence, genomics, new materials, cloud computing, blockchain, the sharing and gig economies, and many other new, advanced technologies and business models signaled the start of the Fourth Industrial Revolution. In that time, tax law and accounting practices have struggled to keep pace with innovations, sometimes leading to a wild West free for all for businesses and consumers, some of who managed to profit while others lost or were swindled of their life savings. Even today tax laws still fail to address the issues that the Age of Computer and the Internet brought, such as the internet sales tax, which may be heading to the US Supreme Court in the next year or so. As President Trump and the U.S. Congress are poised to pass and celebrate the passage of reforming the U.S. tax code for the first time in 30 years, missing from the discussion by both parties is how tax law and accounting principles should be altered to account for the realities of how AI, automation and the Fourth Industrial Revolution are reshaping businesses and the labor markets today and will continue to transform them in the years to come.