An enduring access-to-justice crisis leaves most low- and middle-income people without meaningful assistance for civil legal problems. In response, several U.S. jurisdictions have experimented with licensing legal paraprofessionals—such as Limited License Legal Technicians (LLLTs)—to provide a circumscribed set of services directly to the public. Using Washington State’s pioneering LLLT program and its successors as a case study, this Article argues that paraprofessional reforms have under-delivered because they replicate key features of the traditional professional model: substantial educational prerequisites, supervised practice requirements, and high-stakes examinations that raise entry costs, limit supply, and constrain scalability.

The Article contends that modern AI changes the production function of routine legal work—particularly client intake, document preparation, and the translation of facts into legally relevant narratives—yet AI deployed directly to consumers poses serious risks, including error, bias, confidentiality threats, and jurisdictional mismatch, and it cannot reliably identify when a matter requires escalation to a lawyer. The Article therefore proposes an “AI–paraprofessional fusion” model: purpose-built, jurisdiction-specific AI tools paired with lightly trained human paraprofessionals who provide process guidance, verify and quality-control outputs, and triage cases for escalation when warranted.

Finally, because unauthorized-practice rules are state-created constraints that helped produce today’s scarcity, the Article argues that the AI infrastructure enabling this model should be developed and maintained as a public good—auditable, updateable, and broadly accessible—rather than left solely to private market incentives. This approach offers a scalable path for United States jurisdictions—and potentially others—to expand competent, lower-cost legal assistance while preserving safety through human oversight and clear escalation channels.

Should we adopt broad, anticipatory regulatory frameworks for AI to address its wide-ranging societal impacts? This Article argues that such a universal approach is misguided. Instead, it contends that we should adopt a two-track regulatory strategy that maintains agnosticism about AI’s uncertain long-term trajectory while implementing targeted interventions to address specific, documented harms in the present. The Article develops this argument through a case study of generative AI systems in digital media, examining how these technologies are reshaping the algorithmic dynamics of the public sphere. Tracing the ways in which AI-powered content generation and curation are amplifying long-standing challenges like the erosion of shared epistemic foundations, the entrenchment of echo chambers, and the concentration of informational power, the Article highlights the limitations of the EU’s horizontal AI Act and the potential of China’s more focused, domain-specific approach to regulating these harms. Drawing on this analysis, it makes the case for a hybrid governance model that combines targeted oversight of AI’s known dangers with a continuing spirit of regulatory humility and openness toward its transformative potential. In an age of rapid technological change, this balancing act offers a path beyond the false choice between reactive overregulation and reckless laissez-faire - one that remains attuned to AI’s contextual nuances while advancing the public interest.

The use of copyrighted works in training large AI models has sparked numerous lawsuits globally. This Article examines China’s evolving regulatory landscape, and analyzes two academic proposals for China’s Artificial Intelligence Law, identifying key areas of divergence and consensus regarding the fair use of copyrighted works in AI training. By comparing three different legal approaches to characterizing AI model training, this Article argues that this process qualifies as fair use. This is because machine learning leverages vast corpora to internalize underlying linguistic and creative patterns, rather than storing or directly reproducing the protected works. As a result, the use of copyrighted material in the training phase qualifies as incidental reproduction and transformative use, which, according to our empirical study, does not unreasonably harm the legitimate rights and interests of copyright holders. Furthermore, given the market failure in AI model training licensing, this Article contends that recognizing AI model training as fair use better aligns with China’s legal framework and the practical needs of technological development. To ensure legal certainty, this Article proposes introducing a machine learning exception within either the ongoing revision of the Regulations for the Implementation of the Copyright Law, or future AI legislation in China.

The rapid advancement of generative artificial intelligence (GenAI) presents new challenges and implications for anti-discrimination law. While much of the legal discourse so far has focused on general AI governance, there is a growing need to recognize that different technical models—ranging from algorithmic decision-making systems (ADS) to GenAI—pose distinct risks and require tailored regulatory responses. This Article serves as a foundational introduction to this issue, highlighting the diverse ways in which GenAI can both reinforce and mitigate discrimination and where (European) anti-discrimination law stands in the face of these challenges. The primary goal is to raise awareness of the crucial interplay between AI architectures and legal frameworks. By sketching the underlying technical and structural mechanisms this Article aims to foster an understanding of the potential harmful impact GenAI can have, and how this is met by an anti-discrimination law, not yet equipped for these new “actors.” Thus, it will reveal weaknesses that will need to be addressed by research and policy alike.

Artificial intelligence is increasingly interwoven with design thinking (DT), yet comparative, stage-by-stage syntheses across canonical DT models remain scarce. This literature review maps how AI augments and challenges the major stages of widely used models and relates these effects to five illustrative domains. Following the SPAR-4-SLR protocol, we searched the Web of Science (2005–August 2025), screened records in two stages and assembled a corpus of 205 eligible studies for comparative synthesis. Across models, AI scales early-stage evidence work through large-N text and behavioral analytics, widens ideation via generative systems and accelerates prototyping and testing through simulation and predictive evaluation; at the same time, risks include bias, privacy and sovereignty concerns, evaluation opacity and homogenization of creative output. The weight of evidence supports hybrid intelligence: allocate divergent exploration primarily to AI while retaining human judgment for convergent selection and ethical decision-making. A complementary AI-native “Stingray” model highlights concurrent train–develop–iterate workflows that treat AI as a co-designer, while underscoring governance needs around interpretability and auditability. Overall, the review offers a model-by-model, stage-specific map of AI’s roles in DT, along with practical guidance for responsible deployment and research priorities for assessing boundary conditions and external validity.

Generative artificial intelligence (GenAI) applications hold transformative potential for non-governmental organizations (NGOs) in addressing social issues. However, the integration of GenAI applications in NGOs is challenging due to the presence of several barriers. Contemporary research has not investigated barriers to GenAI adoption within the context of NGOs. Thus, this study aims to explore and prioritize these barriers to GenAI adoption in NGOs. Our study adopts a three-phase mixed methods research approach. First, GenAI adoption barriers were identified through a comprehensive review of extant literature. Second, a deductive approach was used to finalize the most relevant barriers in context of NGOs. Finally, these barriers were prioritized using a level-based weight assessment method. Results reveal fourteen barriers to GenAI adoption in NGOs. The key barriers include lack of awareness about benefits of GenAI, lack of trust in AI, inadequate training opportunities, limited digital infrastructure, and lack of technical expertise. These barriers reflect sociocultural and ethical challenges that prevent NGOs from realizing benefits of GenAI. Our study is one of the first to provide insights into GenAI adoption barriers among NGOs in India. Prioritization of these barriers provides a systematic approach to overcome them and improve NGOs’ efficiency. Findings highlight the need for a forward-thinking mindset duly supported by a bold and strategic approach to change management in order to promote the GenAI adoption in NGOs. Finally, the study contributes to limited literature on intersection of NGOs and GenAI and provides useful implications for practitioners and policymakers.

Generative artificial intelligence (GenAI) shows promise for mental healthcare by increasing access to treatment. In this article, we analyse recent evidence on the use of GenAI chatbots as a treatment for common mental disorders. We examine key ethical and methodological considerations and discuss the specific risks for delusions. Adopting a precision psychiatry perspective, we propose that the therapeutic alliance can be improved by tailoring GenAI to mimic a user’s psychological traits, a version of socioaffective alignment.

Housing affordability is one of the main aspects required for sustainable development and society. However, the timely delivery of new homes is often constrained by the need to upgrade and expand essential infrastructure such as water and electricity networks. For water utilities, responses to growth typically involve intensive hydraulic analysis to assess water distribution systems (WDS) capacity, identify upgrade needs and evaluate options for system extensions. This process becomes significantly complex and resource-intensive under high growth conditions, where a higher volume of faster answers is required to address a wide range of uncertain future scenarios. This paper presents a concept of using generative artificial intelligence (Gen AI) integrating with hydraulic models to form an AI Agent to support WDS design. Specific features of Gen AI used within the hydraulic agent are discussed. A real-life case study demonstrated that the AI agent can analyse land development requests, trigger hydraulic simulations and identify augmentation needed, significantly reducing manual tasks. This offers a breakthrough strategy for water distribution system design and planning to enable sustainable water infrastructure development.

Given the potential of generative artificial intelligence (GenAI) to create human clones, it is not surprising that chatbots have been implemented in politics. In a turbulent political context, these AI-driven bots are likely to be used to spread biased information, amplify polarisation, and distort our memories. Large language models (LLMs) lack ‘political memory’ and cannot accurately process political discourses that draw from collective political memory. We refer to research concerning collective political memory and AI to present our observations of a chatbot experiment undertaken during the Presidential Elections in Finland in early 2024. This election took place at a historically crucial moment, as Finland, traditionally an advocate of neutrality and peacefulness, had become a vocal supporter of Ukraine and a new member state of NATO. Our research team developed LLM-driven chatbots for all presidential candidates, and Finnish citizens were afforded the chance to engage with these chatbot–politicians. In our study, human–chatbot discussions related to foreign and security politics were especially interesting. While rhetorically very typical and believable in light of real political speech, chatbots reorganised prevailing discourses generating responses that distorted the collective political memory. In actuality, Russia’s full-scale invasion of Ukraine had drastically changed Finland’s political positioning. Our AI-driven chatbots, or ‘electobots’, continued to promote constructive dialogue with Russia, thus earning our moniker ‘Finlandised Bots’. Our experiment highlights that training AI for political purposes requires familiarity with the prevailing discourses and attunement to the nuances of the context, showcasing the importance of studying human–machine interactions beyond the typical viewpoint of disinformation.

Extant work shows that generative AI such as GPT-3.5 and perpetuate social stereotypes and biases. A less explored source of bias is ideology: do GPT models take ideological stances on politically sensitive topics? We develop a novel approach to identify ideological bias and show that it can originate in both the training data and the filtering algorithm. Using linguistic variation across countries with contrasting political attitudes, we evaluate average GPT responses in those languages. GPT output is more conservative in languages conservative societies (polish) and more liberal in languages used in liberal ones (Swedish). These differences persist from GPT-3.5 to GPT-4. We conclude that high-quality, curated training data are essential for reducing bias.

Central to drawn representations of activism and memory are ideas of embodiment and trace. From DIY protest signs to craftivism, the articulation of protest and memory is connected to the handmade trace of a witnessing individual present in time and place. This is reflected in comics scholarship through the notion of the drawn line conveying subjective experience through the trace of the body.

This article will consider the relationship between witnessing, truth claims, autographic drawing, and memory at a moment when AI image-generation tools have called into question the connection of drawn traces to their origin in time, space, materiality, and the body.

Although a combination of critical AI theory and comics studies, this article will outline ways in which generative AI presents a challenge to these ideas. Through comparison of Joe Sacco’s graphic reportage with recent AI images of conflict and history, the article considers the truth claims of images that are the products of computational and algorithmic processes considered broadly.

Comics scholarship has been slow to critically respond to these new conditions, and the task of disentangling the human/non-human in ontologies of trace is now compounded by generative drawings, which represent the outcome of archival reappropriation defined by opaque algorithmic parameters. This article will explore theoretical assumptions around authenticity and truth claims in analogue, computational, algorithmic, and generative drawing practice and ask what kinds of theory and practice are appropriate if activist graphic memoir is to endure as documents of political memory.