Artificial Intelligence (AI) is rapidly transforming the landscape of academic law libraries worldwide, offering new opportunities for enhancing legal research, information management, and user engagement. This article examines emerging trends in AI applications within academic law libraries, focusing on global developments alongside the unique challenges and opportunities faced in the Caribbean context. Key areas of exploration include AI-powered legal research tools, natural language processing (NLP) applications, and the ethical considerations surrounding AI integration. Drawing from insights presented at the CARALL Conference in July 2024, this article provides a comparative analysis of global best practices and proposes strategic recommendations for Caribbean academic law libraries to harness the potential of AI while addressing regional gaps in technological infrastructure and AI literacy.

Artificial Intelligence (AI) has enriched the lives of people around the globe. However, the emergence of AI-powered lethal autonomous weapon systems (LAWS) has become a significant concern for the international community. LAWS are computer-based weapon systems capable of completing their missions, including identifying and engaging targets without direct human intervention. The use of such weapons poses significant challenges to compliance with international humanitarian and human rights law. Scholars have extensively examined LAWS in the context of humanitarian law; however, their implications for human rights warrant further discussion. Against this backdrop, this paper analyzes the human rights challenges posed by LAWS under international law. It argues that using LAWS in warfare and domestic law enforcement operations could violate human rights, such as the rights to life, human dignity, and remedy, among others. Thus, it calls for a prohibition of the use of killer robots against humans.

As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent, but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I'll instead make the case that consciousness depends on our nature as living organisms – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is, or convincingly appears to be, conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate ourselves.

Africa had a busy election calendar in 2024, with at least 19 countries holding presidential or general elections. In a continent with a large youth population, a common theme across these countries is a desire for citizens to have their voices heard, and a busy election year offers an opportunity for the continent to redeem its democratic credentials and demonstrate its leaning towards strengthening free and fair elections and a more responsive and democratic governance. Given the central role that governance plays in security in Africa, the stakes from many of these elections are high, not only to achieve a democratically elected government but also to achieve stability and development. Since governance norms, insecurity, and economic buoyancy are rarely contained by borders, the conduct and outcomes from each of these elections will also have implications for neighbouring countries and the continent overall. This article considers how the results of recent elections across Africa have been challenged in courts based on mistrust in the use of technology platforms, how the deployment of emerging technology, including AI, is casting a shadow on the integrity of elections in Africa, and the policy options to address these emerging trends with a particular focus on governance of AI technologies through a human rights-based approach and equitable public procurement practices.

Large language models (LLMs) such as ChatGPT, Gemini, and Claude are increasingly being used in aid or place of human judgment and decision making. Indeed, academic researchers are increasingly using LLMs as a research tool. In this paper, we examine whether LLMs, like academic researchers, fall prey to a particularly common human error in interpreting statistical results, namely ‘dichotomania’ that results from the dichotomization of statistical results into the categories ‘statistically significant’ and ‘statistically nonsignificant’. We find that ChatGPT, Gemini, and Claude fall prey to dichotomania at the 0.05 and 0.10 thresholds commonly used to declare ‘statistical significance’. In addition, prompt engineering with principles taken from an American Statistical Association Statement on Statistical Significance and P-values intended as a corrective to human errors does not mitigate this and arguably exacerbates it. Further, more recent and larger versions of these models do not necessarily perform better. Finally, these models sometimes provide interpretations that are not only incorrect but also highly erratic.

Skeptical theism attempts to address the problem of evil by appealing to human cognitive limitations. The causal structure of the world is opaque to us. We cannot tell, and should not expect to be able to tell, if there is gratuitous evil, that is, evil which isn’t necessary for achieving some greater good or for precluding some greater evil. At first, it seems tempting to think that the rapid development of artificial intelligence (AI) technologies might change this fact. Our cognitive limitations may no longer be a fixed point in responding to the problem of evil. But I argue that this won’t ultimately matter. The workings of any AI capable of rendering the problem of evil tractable will likely be just as opaque to us as the causal structure of the world God created. Interestingly, then, both God and a sufficiently advanced AI are alien intelligences to us. This reveals what is truly difficult, and perhaps intractable, about the problem of evil. For the evils to be defeated, we would need a relational understanding of why God permitted them. Yet such an understanding of an inscrutable God seems forever beyond our, and even our post-human descendants, ken.

Prior research has shown that people judge algorithmic errors more harshly than identical mistakes made by humans—a bias known as algorithm aversion. We explored this phenomenon across two studies (N = 1199), focusing on the often-overlooked role of conventionality when comparing human versus algorithmic errors by introducing a simple conventionality intervention. Our findings revealed significant algorithm aversion when participants were informed that the decisions described in the experimental scenarios were conventionally made by humans. However, when participants were told that the same decisions were conventionally made by algorithms, the bias was significantly reduced—or even completely offset. This intervention had a particularly strong influence on participants’ recommendations of which decision-maker should be used in the future—even revealing a bias against human error makers when algorithms were framed as the conventional choice. These results suggest that the existing status quo plays an important role in shaping people’s judgments of mistakes in human–algorithm comparisons.

This article presents a novel conversational artificial intelligence (CAI)-enabled active ideation system as a creative idea generation tool to assist novice product designers in mitigating the initial latency and ideation bottlenecks that are commonly observed. It is a dynamic, interactive, and contextually responsive approach, actively involving a large language model (LLM) from the domain of natural language processing (NLP) in artificial intelligence (AI) to produce multiple statements of potential ideas for different design problems. Integrating such AI models with ideation creates what we refer to as an active ideation scenario, which helps foster continuous dialog-based interaction, context-sensitive conversation, and prolific idea generation. An empirical study was conducted with 30 novice product designers to generate multiple ideas for given problems using traditional methods and the new CAI-based interface. The ideas generated by both methods were qualitatively evaluated by a panel of experts. The findings demonstrated the relative superiority of the proposed tool for generating prolific, meaningful, novel, and diverse ideas. The interface was enhanced by incorporating a prompt-engineered structured dialog style for each ideation stage to make it uniform and more convenient for the product designers. A pilot study was conducted and the resulting responses of such a structured CAI interface were found to be more succinct and aligned toward the subsequent design stage. The article thus established the rich potential of using generative AI (Gen-AI) for the early ill-structured phase of the creative product design process.

One of the most significant challenges in research related to nutritional epidemiology is the achievement of high accuracy and validity of dietary data to establish an adequate link between dietary exposure and health outcomes. Recently, the emergence of artificial intelligence (AI) in various fields has filled this gap with advanced statistical models and techniques for nutrient and food analysis. We aimed to systematically review available evidence regarding the validity and accuracy of AI-based dietary intake assessment methods (AI-DIA). In accordance with PRISMA guidelines, an exhaustive search of the EMBASE, PubMed, Scopus and Web of Science databases was conducted to identify relevant publications from their inception to 1 December 2024. Thirteen studies that met the inclusion criteria were included in this analysis. Of the studies identified, 61·5 % were conducted in preclinical settings. Likewise, 46·2 % used AI techniques based on deep learning and 15·3 % on machine learning. Correlation coefficients of over 0·7 were reported in six articles concerning the estimation of calories between the AI and traditional assessment methods. Similarly, six studies obtained a correlation above 0·7 for macronutrients. In the case of micronutrients, four studies achieved the correlation mentioned above. A moderate risk of bias was observed in 61·5 % (n 8) of the articles analysed, with confounding bias being the most frequently observed. AI-DIA methods are promising, reliable and valid alternatives for nutrient and food estimations. However, more research comparing different populations is needed, as well as larger sample sizes, to ensure the validity of the experimental designs.