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AI and the law: assistant or assassin?
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AI and the Law: assistant or assassin? By Ursula Smartt Northeastern University London
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various fields at an unprecedented pace, with profound implications for industries, such as law. AI, first defined in 1956 by John McCarthy, refers to machines imitating human-like behaviours, while ML is a subset where systems ‘learn’ from data to make decisions. Artificial Neural Networks (ANNs), a key example of ML, are designed to mimic the human brain’s structure to discern patterns, used notably in music recommendation algorithms. Despite their potential, challenges arise in intellectual property (IP) law, as demonstrated in the Emotional Perception AI Ltd case in 2024, where the London Court of Appeal Court (CA) ruled against granting a patent to an ANN, highlighting legal debates on whether AI-generated inventions should be recognized as intellectual property.
The issue extends to Large Language Models (LLMs), such as ChatGPT or Gemini, which predict and generate human-like text but often suffer from inconsistencies or ‘hallucinations.’ Another contentious area is whether AI can be considered sentient. While some argue that the neural network design of LLMs might lead to artificial consciousness, others claim that AI simply simulates understanding without true cognition.
In the realm of patents, the case of DABUS, an AI that independently generates inventions, has sparked global debate on whether AI systems can be granted patent rights. Courts have largely rejected AI as inventors, though some countries, such as South Africa, have granted patents recognizing AI’s legal personhood.
Generative AI’s rise also challenges copyright laws, particularly in music creation, as AI tools generate content from pre-existing works. Legal disputes, such as those involving Universal Music Group and AI firms, highlight the complexities of ownership and infringement, raising fundamental questions about the future of copyright in an AI-driven world
Can Dabus Now have a German Passport?
No full textThe case commentary looks at the latest ruling in the DABUS-saga, this time from the German Federal Court in Karlsruhe. Since 2017 Dr Stephen Thaler and his attorney Professor Ryan Abbott have tried to prove that patent protection should be available for innovation invented by AI. Together they have filed a long series of world-wide high-profile legal actions, including the UK, US, New Zealand, Australia, Saudi Arabia and South Africa. All with the same aim to have Thaler’s DABUS device listed as the inventor with its own patent. This article follows on from an earlier piece EIPR (2024), with an update from the German Federal Court (Bundesgerichtshof 11 June 2024). DABUS operator and inventor, Dr Stephen L. Thaler, has been trying for years to have AI registered as an inventor worldwide - and has so far failed in almost all cases worldwide except Saudi Arabia and South Africa
Investor Action on Health: A Review
No full textEXECUTIVE SUMMARY
1. Using a system-level approach, we review the different mechanisms through which
investors can contribute to improved population health outcomes. Specifically, we
highlight how these investor action mechanisms – including corporate engagement,
environmental, social and governance (ESG) ratings, board oversight, and policy engagement
– have been used by institutional investors (i.e., asset owners, asset managers) to advance
15 priority health issues, ranging from food safety and alcohol harm to air pollution and worker
health.
2. We categorize the 15 priority health issues according to their maturity from the
perspective of the investment community. Investors are motivated to use the full spectrum
of mechanisms to address mature issues (e.g., human rights, tobacco smoking) and these are
actively incorporated into investment decisions and stewardship activities, as there is general
consensus about the financial materiality of these issues. Progressing issues (e.g., nutrition,
access to medicines) are growing in significance within the investment community and involve
a diverse yet underused array of mechanisms. Emerging issues (e.g., digital well-being,
access to quality housing) have only recently begun to attract some attention from investors
who are beginning to recognize potential financial risks associated with these issues.
3. We propose that investors need to recognize the maturity of the issue when deciding
which mechanisms to use. By matching the right mechanism to the maturity of the issue,
investors are more likely to further advance the relevance of the issue in the broader
investment community. Drawing on lessons from other ESG issues, including climate change
and diversity, equity and inclusion, we develop a framework that can be applied to investor
action on health-related issues. Using this framework allows investors to understand which
actions are most likely to be relevant for each issue given its stage of maturity.
4. The complexities associated with population health and the financial system provide
challenges and opportunities for investors. We detail five different challenges relating to
investor action on health:
1. Issue scope: The meaningful differences between different types of health-related issues
means that investor action needs to be designed to fit with the characteristics of each
issue.
2. Defining impact: The goals associated with investor action on health will ideally be
measurable and attributable to investors’ efforts.
3. Impact time lags: Many of the desired impacts of investor action on health will take time
to be implemented so investors will need to identify realistic timeframes and key
milestones for different types of outcomes and impacts.
4. Demonstrating financial materiality: Existing financial materiality assessment
frameworks place varying emphasis on health-related issues so motivated investors may
need to play an educational role to raise the profile of less mature issues.
5. Considering system-level effects: Although the investment system is complex, investors
can identify key leverage points in the system to unlock wider support for their efforts.
Despite the barriers posed by these challenges, we highlight that investors have opportunities
to carefully design their actions to increase their effectiveness when seeking to positively
contribute to population health
Teaching London’s Past Today: An Experiential Approach to a Global City
No full textThis article provides an account of teaching London’s cultural history on a semester-long, first-year undergraduate study-abroad course at Northeastern University London (NUL) using a multi-authored, case-study approach. It consists of an extended introduction by the course leader and a course instructor followed by seven contributions from current and former instructors, most of whom are still working at NUL, discussing examples of best practice in experiential learning in the humanities. Its intended audiences are teachers and lecturers of English and visual culture, of London, of pedagogy, and of other kinds of learning with a local, place-bound scope, as well as readers interested generally in transmitting London’s cultural past to learners and citizens in the present
Exploring the Effectiveness of Bite-Sized Learning for Statistics via TikTok
Full text linkBusiness statistics is an essential course in the curriculum of business degree programs. Statistics is traditionally a challenging course that may be met with trepidation by many students. A pedagogical approach known as bite-sized learning has gained popularity to alleviate the difficulties associated with comprehending complex concepts. This approach delivers content in manageable increments, reducing cognitive burdens. In tandem, instructors can leverage social media platforms to deliver bite-sized content, making the learning process more engaging and accessible. Among these platforms, TikTok, a widely popular social media platform, resonates with students for familiarity and engagement making it an ideal tool for delivering educational content. This research sought to investigate the effectiveness of implementing bite-sized learning strategies via TikTok within a business statistics course. The study divided students into treatment and control groups, administering pre-tests and post-tests. The treatment group, receiving TikTok supplemental material, demonstrated significantly higher scores than the control group, suggesting the efficacy of bite-sized lessons through TikTok in improving student performance. Moreover, the flexibility of using short-form videos as a supplemental tool makes them an effective resource for both in-class and online or distance learning environments, where students can engage with the material at their own pace
Inferring contact network characteristics from epidemic data via compact mean-field models
Full text linkModelling epidemics using contact networks provides a significant improvement over classical
compartmental models by explicitly incorporating the network of contacts. However, while
network-based models describe disease spread on a given contact structure, their potential for
inferring the underlying network from epidemic data remains largely unexplored. In this work,
we consider the edge-based compartmental model (EBCM), a compact and analytically tractable
framework, and we integrate it within dynamical survival analysis (DSA) to infer key network
properties along with parameters of the epidemic itself. Despite correlations between structural
and epidemic parameters, our framework demonstrates robustness in accurately inferring
contact network properties from synthetic epidemic simulations. Additionally, we apply the
framework to real-world outbreaks—the 2001 UK foot-and-mouth disease outbreak and the
COVID-19 epidemic in Seoul— to estimate both disease parameters and network characteristics.
Our results show that our framework achieves good fits to real-world epidemic data and
reliable short-term forecasts. These findings highlight the potential of network-based inference
approaches to uncover hidden contact structures, providing insights that can inform the design
of targeted interventions and public health strategies
A pair-based approximation for simplicial contagion
No full textHigher-order interactions play an important role in complex contagion processes. Mean-field
approximations have been used to characterize the onset of spreading in the presence of group interactions. However, individual-based mean-field models are unable to capture correlations between
different subsets of nodes, which can significantly influence the dynamics of a contagion process. In
this paper, we introduce a pair-based mean-field approximation that allows to study the dynamics
of a SIS model on simplicial complexes by taking into account correlations at the level of pairs of
nodes. Compared to individual-based mean-field approaches, the proposed approximation yields
more accurate predictions of the dynamics of contagion processes on simplicial complexes. Specifically, the pair-based mean-field approximation provides higher accuracy in predicting the extent of
the region of bistability, the type of transition from disease-free to endemic state, and the average
time evolution of the fraction of infected individuals. Crucially, for the pair-based approximation we
were able to obtain an analytical expression for the epidemic threshold, that elucidates the dependency on the parameters of the model. Through comparison with stochastic simulations, we show
that our model correctly predicts that the onset of the epidemic outbreak in simplicial complexes
depends on the strength of higher-order interactions. Overall, our findings highlight the importance of accounting for pair correlations when investigating contagion processes in the presence of
higher-order interactions
Computational Analysis for Philosophical Education: A Case Study in AI Ethics
Full text linkThis paper explores what computational methodologies can tell us about philosophical education particularly in the context of AI Ethics. Taking the readings on our AI Ethics and Responsible AI syllabi as a corpus of AI ethics literature, we conduct an analysis of the content of these courses through a variety of methods: word frequency analysis, TF-IDF scoring, document vectorization via SciBERT, clustering via K-means, and topic modelling using Latent Dirichlet Allocation (LDA). We reflect on the findings of these analyses, and more broadly on what computational approaches can offer to the practice of philosophical education. Finally, we compare our approach to previous computational approaches in philosophy, and more broadly in the digital humanities. This project offers a proof-of-concept for how contemporary NLP techniques can be used to support philosophical pedagogy: not only to reflect critically on what we teach, but to discover new materials, explore conceptual gaps, and make our courses more accessible to students from a range of disciplinary backgrounds
A Social History of Analytic Philosophy: How Politics Has Shaped an Apolitical Philosophy
No full textAnalytic philosophy is the leading form of philosophy in the English-speaking world. What explains its continued success? Christoph Schuringa argues that its enduring power can only be understood by examining its social history. Analytic philosophy tends to think of itself as concerned with eternal questions, transcending the changing scenes of history. It thinks of itself as apolitical. This book, however, convincingly shows that the opposite is true.
The origins of analytic philosophy are in a set of distinct movements, shaped by high-ly specific sets of political and social forces. Only after the Second World War were these disparate, often dynamic movements joined together to make ‘analytic philosophy’ as we know it. In the climate of McCarthyism, analytic philosophy was robbed of political force.
To this day, analytic philosophy is the ideology of the status quo. It may seem arcane and largely removed from the real world, but it is a crucial component in upholding liberalism, through its central role in elite educational institutions. As Schuringa concludes, the apparently increasing friendliness of analytic philosophers to rival approaches in philosophy should be understood as a form of colonization; thanks to its hegemonic status, it reformats all it touches in service of its own imperatives, going so far as to colonize decolonial efforts in the discipline
The impact of the Russia-Ukraine war on global supply chains: a systematic literature review
Full text linkThis systematic review examines the multifaceted impacts of the Russia-Ukraine war on global supply chains. Following PRISMA methodology, we analyze 22 peer-reviewed studies published between 2022 and 2025 to identify key disruption patterns, sectoral vulnerabilities, regional impacts, and adaptive strategies. Our findings reveal significant disruptions across food, energy, and critical materials sectors, with asymmetric regional vulnerabilities particularly affecting developing economies. The review identifies five major impact domains: (1) food security disruptions, (2) energy market volatility, (3) critical material shortages, (4) transportation bottlenecks, and (5) financial market responses. We document emerging adaptation strategies including supply diversification, strategic reserves development, and accelerated digitalization. The findings suggest permanent shifts in global supply chain configurations and trade relationships that will persist beyond the conflict’s resolution. This review contributes to both academic understanding of supply chain vulnerability to geopolitical shocks and provides practical insights for logistics professionals developing resilience strategies