Advanced intelligent systems, particularly Large Language Models (LLMs), are significantly reshaping financial practices through advancements in Natural Language Processing (NLP). However, the extent to which these models effectively capture and apply domain-specific financial knowledge remains uncertain. Addressing a critical gap in the expansive Indian financial context, this paper introduces CA-Ben, a Chartered Accountancy benchmark specifically designed to evaluate the financial, legal, and quantitative reasoning capabilities of LLMs. CA-Ben comprises structured question-answer datasets derived from the rigorous examinations conducted by the Institute of Chartered Accountants of India (ICAI), spanning foundational, intermediate, and advanced CA curriculum stages. Six prominent LLMs i.e. GPT 4o, LLAMA 3.3 70B, LLAMA 3.1 405B, MISTRAL Large, Claude 3.5 Sonnet, and Microsoft Phi 4 were evaluated using standardized protocols. Results indicate variations in performance, with Claude 3.5 Sonnet and GPT-4o outperforming others, especially in conceptual and legal reasoning. Notable challenges emerged in numerical computations and legal interpretations. The findings emphasize the strengths and limitations of current LLMs, suggesting future improvements through hybrid reasoning and retrieval-augmented generation methods, particularly for quantitative analysis and accurate legal interpretation.

--Economic inequality is a global challenge, intensifying disparities in education, healthcare, and social stability. Traditional systems like the U.S. federal income tax reduce inequality but lack adaptability. Although models like the Saez Optimal T axation adjust dynamically, they fail to address taxpayer heterogeneity and irrational behavior . This study introduces T axAgent, a novel integration of large language models (LLMs) with agent-based modeling (ABM) to design adaptive tax policies. In our macroeconomic simulation, heterogeneous H-Agents (households) simulate real-world taxpayer behaviors while the T axAgent (government) utilizes LLMs to iteratively optimize tax rates, balancing equity and productivity. Benchmarked against Saez Optimal T axation, U.S. federal income taxes, and free markets, T axAgent achieves superior equity-efficiency tradeoffs. This research offers a novel taxation solution and a scalable, data-driven framework for fiscal policy evaluation. Economic inequality is a critical global issue with profound social, political, and economic impacts. Research highlights its detrimental effects on education, healthcare, political stability, and economic growth[1, 2, 3].

The use of artificial intelligence (AI) in the public sector is best understood as a continuation and intensification of long standing rationalization and bureaucratization processes. Drawing on Weber, we take the core of these processes to be the replacement of traditions with instrumental rationality, i.e., the most calculable and efficient way of achieving any given policy objective. In this article, we demonstrate how much of the criticisms, both among the public and in scholarship, directed towards AI systems spring from well known tensions at the heart of Weberian rationalization. To illustrate this point, we introduce a thought experiment whereby AI systems are used to optimize tax policy to advance a specific normative end, reducing economic inequality. Our analysis shows that building a machine-like tax system that promotes social and economic equality is possible. However, it also highlights that AI driven policy optimization (i) comes at the exclusion of other competing political values, (ii) overrides citizens sense of their noninstrumental obligations to each other, and (iii) undermines the notion of humans as self-determining beings. Contemporary scholarship and advocacy directed towards ensuring that AI systems are legal, ethical, and safe build on and reinforce central assumptions that underpin the process of rationalization, including the modern idea that science can sweep away oppressive systems and replace them with a rule of reason that would rescue humans from moral injustices. That is overly optimistic. Science can only provide the means, they cannot dictate the ends. Nonetheless, the use of AI in the public sector can also benefit the institutions and processes of liberal democracies. Most importantly, AI driven policy optimization demands that normative ends are made explicit and formalized, thereby subjecting them to public scrutiny and debate.

More than anything, Star Wars: Episode 1 - The Phantom Menace is a fascinating cultural object. It's been 25 years since I saw the film in theaters, and over a decade since I last rewatched it (in a vain attempt to help my Trekkie wife catch up to the prequels). I've had enough time to process the initial disappointment and embarrassment of introducing my wife to Jar Jar Binks. So when Disney announced it was bringing the prequel trilogy back to theaters, I was practically giddy about revisiting them to see how George Lucas's final films compared to the onslaught of Star Wars media we've experienced over the past decade. Was The Phantom Menace as bad as I'd remembered?

We consider the problem of repeatedly choosing policies to maximize social welfare. Welfare is a weighted sum of private utility and public revenue. Earlier outcomes inform later policies. Utility is not observed, but indirectly inferred. Response functions are learned through experimentation. We derive a lower bound on regret, and a matching adversarial upper bound for a variant of the Exp3 algorithm. Cumulative regret grows at a rate of $T^{2/3}$. This implies that (i) welfare maximization is harder than the multi-armed bandit problem (with a rate of $T^{1/2}$ for finite policy sets), and (ii) our algorithm achieves the optimal rate. For the stochastic setting, if social welfare is concave, we can achieve a rate of $T^{1/2}$ (for continuous policy sets), using a dyadic search algorithm. We analyze an extension to nonlinear income taxation, and sketch an extension to commodity taxation. We compare our setting to monopoly pricing (which is easier), and price setting for bilateral trade (which is harder).

In today's world, the power of artificial intelligence is everywhere. From agriculture to healthcare, from shopping to dating, from the vehicles we drive to the way we do business, our experiences are increasingly shaped by AI. This is true even when it comes to whiskey tasting, although in this case the intelligence is driven by our senses and our reasoning rather than sophisticated algorithms. This is a topic that is close to my heart, given that I'm a director of AI, data analytics and high performance computing sales who moonlights as a whiskey sommelier. I often have occasion to reflect on the amazing parallels between the principles of AI and the process of tasting whisky.

A pandemic is raging with devastating consequences, and long-standing problems with racial bias and political polarization are coming to a head. Artificial intelligence (AI) has the potential to help us deal with these challenges. However, AI's risks have become increasingly apparent. Scholarship has illustrated cases of AI opacity and lack of explainability, design choices that result in bias, negative impacts on personal well-being and social interactions, and changes in power dynamics between individuals, corporations, and the state, contributing to rising inequalities. Whether AI is developed and used in good or harmful ways will depend in large part on the legal frameworks governing and regulating it.

Income inequality is one of the overarching problems of economics. One of the most effective tools policymakers have to address it is taxation: governments collect money from people according to what they earn and redistribute it either directly, via welfare schemes, or indirectly, by using it to pay for public projects. But though more taxation can lead to greater equality, taxing people too much can discourage them from working or motivate them to find ways to avoid paying--which reduces the overall pot. Getting the balance right is not easy. Economists typically rely on assumptions that are hard to validate.

Tax policy is surely a complex beast, and depending on your political leanings, you probably have some strong feelings about how it should be implemented. Salesforce AI researchers are trying to build a model to bring artificial intelligence to bear on what will undoubtedly always be a highly political process. Richard Socher, who heads up AI research at Salesforce, says the company is researching all kinds of solutions related to AI and business, and how it could improve the Salesforce product family; however, he also looks at how his team could use AI to solve a set of broader social issues beyond what it can do for the product line. Socher says when you look at the biggest issues of our time, one of the largest is economic inequality, and how we could use policy to solve that. To that end, the company created a model it calls an AI economist that could look at various economic variables, a broad set of economic models and using the power of AI begin to demonstrate how various policies affect economic inequality versus productivity.

In 1979, an innovative two-minute TV commercial gave Britain a glimpse of the future. Choreographed to music from Rossini's Barber of Seville, hi-tech machines built the Fiat Strada. The tagline was "Handbuilt by Robots." Humans were nowhere to be seen in the Turin factory where the ad was shot, but the film crew knew where the people were: outside, on picket lines protesting the loss of their jobs. Fast forward nearly 40 years and "the robots are coming, they want to replace us, and there's nothing we can do to stop them" isn't the plot of the next season of Westworld, it's a real-world warning that's becoming louder with each new leap in the fields of artificial intelligence (AI) and robotics. Both the technoprogressive enthusiasts and the head-in-the-sand reactionaries believe doomsayers are overstating the threat.