Google has agreed a sweeping deal with the UK government to provide free technology to the public sector from the NHS to local councils– a move campaigners have called "dangerously naive". The US company will be asked to "upskill" tens of thousands of civil servants in technology, including in using artificial intelligence, as part of an agreement which will not require the government to pay. It is considered in Whitehall to be giving Google "a foot in the door" as the digitisation of public services accelerates. However, the agreement prompted concerns about the precariousness of UK public data being held on US servers amid the unpredictable leadership of Donald Trump. The Department of Science, Innovation and Technology (DSIT) said Google Cloud, which provides databases, machine learning and computing power, had "agreed to work with the UK government in helping public services use advanced tech to shake off decades old'ball and chain' legacy contracts which leave essential services vulnerable to cyber-attack". Google's services are considered more agile and efficient than traditional competitors, but there are concerns in Whitehall's digital circles about the government becoming locked into a new kind of dependency.

The perceived quality of the explanations accompanying e-government services is key to gaining trust in these institutions, consequently amplifying further usage of these services. Recent advances in generative AI, and concretely in Large Language Models (LLMs) allow the automation of such content articulations, eliciting explanations' interpretability and fidelity, and more generally, adapting content to various audiences. However, selecting the right LLM type for this has become a non-trivial task for e-government service providers. In this work, we adapted a previously developed scale to assist with this selection, providing a systematic approach for the comparative analysis of the perceived quality of explanations generated by various LLMs. We further demonstrated its applicability through the tax-return process, using it as an exemplar use case that could benefit from employing an LLM to generate explanations about tax refund decisions. This was attained through a user study with 128 survey respondents who were asked to rate different versions of LLM-generated explanations about tax refund decisions, providing a methodological basis for selecting the most appropriate LLM. Recognizing the practical challenges of conducting such a survey, we also began exploring the automation of this process by attempting to replicate human feedback using a selection of cutting-edge predictive techniques.

How capable are large language models (LLMs) in the domain of taxation? Although numerous studies have explored the legal domain in general, research dedicated to taxation remain scarce. Moreover, the datasets used in these studies are either simplified, failing to reflect the real-world complexities, or unavailable as open source. To address this gap, we introduce PLAT, a new benchmark designed to assess the ability of LLMs to predict the legitimacy of additional tax penalties. PLAT is constructed to evaluate LLMs' understanding of tax law, particularly in cases where resolving the issue requires more than just applying related statutes. Our experiments with six LLMs reveal that their baseline capabilities are limited, especially when dealing with conflicting issues that demand a comprehensive understanding. However, we found that enabling retrieval, self-reasoning, and discussion among multiple agents with specific role assignments, this limitation can be mitigated.

'Special Report' anchor Bret Baier discusses Democrats' backlash over Elon Musk's effort to rid the government of wasteful spending, the USAID and CIA's alleged connections to the Trump impeachment and the president's plan for Gaza. As the Trump administration's Department of Government Efficiency (DOGE) works to slash government waste, a bipartisan bill in Congress is aiming to bring the federal government's computer systems "out of the Stone Age." The bipartisan Strengthening Agency Management And Oversight Of Software Assets (SAMOSA) Act passed the House in December, and Sen. Joni Ernset, R-Iowa, is leading efforts to get it passed in the upper chamber. Ernst, the chair of the Senate DOGE Caucus, said the SAMOSA Act will "bring Washington out of the Stone Age and into the 21st century." Fox News Digital is told the bill could potentially save 750 million annually for taxpayers by consolidating federal agencies' cloud computing software licenses.

Fox News' senior national correspondent William La Jeunesse joins'America's Newsroom' to discuss Congress' history of killing pushes for cost-cutting. FIRST ON FOX: The Congressional Department of Government Efficiency (DOGE) Caucus is holding its second-ever meeting on Wednesday, where its leaders are expected to unveil a set of "principles" to guide the group in its mission to cut government waste. They outlined eight goals, some practical while others more symbolic, in a bid to ensure the caucus is in sync with the DOGE advisory panel set up by President-elect Donald Trump. "The federal government must serve the interests of taxpayers, and taxpayers are best served by a lean, efficient, transparent, and accountable bureaucracy," the first principle read, according to a draft memo obtained by Fox News Digital. The document also suggested both lofty and smaller-scale goals.

The recent proliferation of generative artificial intelligence (AI) technologies such as pre-trained large language models (LLMs) has opened up new frontiers in computational law. An exciting area of development is the use of AI to automate the deductive rule-based reasoning inherent in statutory and contract law. This paper argues that such automated deductive legal reasoning can now be viewed from the lens of software engineering, treating LLMs as interpreters of natural-language programs with natural-language inputs. We show how it is possible to apply principled software engineering techniques to enhance AI-driven legal reasoning of complex statutes and to unlock new applications in automated meta-reasoning such as mutation-guided example generation and metamorphic property-based testing.

Leaders of AI companies often argue that AI products will handle mundane tasks, freeing people up to be more productive and creative. And there are few tasks more mundane than taxes. An individual American taxpayer spends roughly 13 hours and 240 out-of-pocket costs just to prepare and file one annual tax return, according to one 2022 study--an estimated 1.15 billion hours collectively spent on tax preparation. So it's not surprising that tax companies have begun rolling out AI-powered tools in an effort to make filing easier. AI-powered tax software, these companies argue, can automate repetitive tasks like data entry, cull through patterns in order to find relevant tax breaks, identify potential compliance risks, and answer tricky questions that filers may have.

The new budget shows the Democrat's priorities. The IRS is warning taxpayers that they may be leaving more than 1 billion on the table. The federal tax collector said Monday that roughly 940,000 people in the U.S. have until May 17 to submit tax returns for unclaimed refunds for tax year 2020, which total more than 1 billion nationwide. The average median refund is 932 for 2020. Texas (93,400), California (88,200), Florida (53,200) and New York (51,400) have the largest number of people potentially eligible for these refunds. JIM JORDAN OPENS INVESTIGATION INTO ACCUSATIONS IRS IS USING AI TO SPY ON TAXPAYERS'EN MASSE' IRS Commissioner Danny Werfel said in a statement: "We want taxpayers to claim these refunds, but time is running out for people who may have overlooked or forgotten about these refunds.

FIRST ON FOX: House Judiciary Chair Jim Jordan, R-Ohio, is launching an investigation alongside Rep. Harriet Hageman, R-Wyo., into whether the IRS is using artificial intelligence (AI) technology to improperly surveil American taxpayers across the country. In a pair of letters sent to Treasury Secretary Janet Yellen and Attorney General Merrick Garland, the lawmakers point to a September 2023 press release in which the IRS said AI "will help IRS compliance teams better detect tax cheating, identify emerging compliance threats and improve case selection tools to avoid burdening taxpayers with needless'no-change' audits." "However, recent reporting alleges that the IRS's use of AI has also included actively monitoring American citizens' bank accounts en masse and without legal process," they wrote, citing a report by James O'Keefe's O'Keefe Media Group. FORMER GOOGLE CONSULTANT SAYS GEMINI IS WHAT HAPPENS WHEN AI COMPANIES GO'TOO BIG TOO SOON' House Judiciary Chairman Jim Jordan is demanding answers from Treasury Secretary Janet Yellen about reports her department is using AI to surveil taxpayers. "Video footage obtained by an investigative media outlet appears to capture Alex Mena, an IRS official working in the agency's Criminal Investigations Unit, admitting that the IRS has'a new system' that uses AI to target'potential abusers' by examining all returns, bank statements, and related financial information for'potential for fraud.' Mena asserted that the new AI system has the ability to access and monitor'all the information from all the companies in the world.'"

As a tax professor, I love taxes: the theory, the policy, even the politics. But I have a confession to make. My taxes are not complicated. Yet, every year, I spend hour upon hour gathering documents, paying for tax preparation software, entering in my income, and puzzling through the instructions as I try to figure out whether I am eligible for this or that deduction or credit. Every year, I think to myself: There has got to be a better way!