http://hdl.handle.net/10453/35206 2026-04-07T18:59:55Z http://hdl.handle.net/10453/193538 Title: He, her, they robot: gendered AI chatbots and the proliferation of technologically facilitated violence Authors: Hook, S; Sassine, J http://hdl.handle.net/10453/193013 Title: Balancing innovation and integrity: AI in tax administration and taxpayer rights Authors: Guglyuvatyy, E Abstract: Artificial intelligence (AI) is transforming tax administration by improving efficiency, compliance, and decision-making. However, this shift raises critical concerns about transparency, accountability, and taxpayer rights. This paper examines how AI-driven systems impact legal fairness, due process, and the integrity of tax procedures. It highlights risks such as algorithmic bias, opacity, and weakened procedural safeguards, while acknowledging AI’s potential to streamline enforcement. To safeguard taxpayer rights, the paper proposes an independent AI oversight mechanism to explain and review tax decisions. This system would enhance transparency, reinforce trust, and ensure legal accountability. The paper calls for regulatory frameworks that embed oversight, uphold public trust, and balance innovation with fundamental legal protections. 2025-11-24T00:00:00Z http://hdl.handle.net/10453/192928 Title: Using Large Language Models in Cluster Analysis in the Social Sciences Authors: Miller, J; Nicholls, R Abstract: The law, regulation, and policy of and for the digital economy can be viewed through different lenses. These include the formal approaches used by lawyers and academics through analysis by news businesses to content shared in video or audio form. Understanding the commonality and differences between the view through each of the lenses requires coordinated data sources. The International Digital Policy Observatory (IDPO) was created to develop a dataset across a variety of sources. This article demonstrates a novel methodological approach that uses data from the IDPO to analyze the interaction between different data sources. It does this using artificial intelligence regulation as an example and combines Gaussian Mixture Model (GMM) clustering techniques with Large Language Models (LLMs) for interpretable cluster naming to identify themes flowing from the data. It sets out the thematic outcomes in the context of each of the data source types to illustrate the method’s utility. This article’s primary contribution is methodological, presenting a scalable and interpretable workflow for analyzing large, multi-source text datasets in social science research. The clustering approach used is likely to be helpful in the analysis of text metadata in other large datasets. 2026-01-01T00:00:00Z http://hdl.handle.net/10453/191782 Title: Simply Impossible Authors: McDonald, D 2014-12-01T00:00:00Z