This article seeks to illuminate the ethical challenges and concerns presented by the utilization of the large language model, ChatGPT. Five critical ethical dimensions as proposed by Laudon and Laudon – Information rights and obligations, Property rights and obligations, Accountability and control, System quality, and Quality of life – will serve as the analytical framework to explore the pertinent issues. Although our investigation revealed that AI technologies like ChatGPT have tremendous potential for societal advancement they also present complex ethical challenges. The implications of our research have impact not only for developers of large language models but also developers of AI technologies in general, policy makers, end-users of these AI applications, and society as whole. Based on our findings we propose key recommendations to address the current concerns with respect to the ethical issues surrounding large language models. By assessing these ethical dimensions within the context of ChatGPT, this paper underscores the importance of developing comprehensive ethical guidelines and policies in the era of increasingly sophisticated AI applications.

Predicting wildfires using Machine Learning models is relevant and essential to minimize wildfire threats to protect human lives and reduce significant property damage. Reliance on traditional wildfire indices for forecasting wildfires has failed to provide the expected prediction outcomes, resulting in limited application of these models. Thus, this research compares the outcome of wildfire forecasting using fire danger rating indices against Machine Learning model outcomes. Furthermore, the performance effectiveness of the fire danger rating indices and Machine Learning model outcomes are assessed using the same wildfire incidents. The One-class Machine Learning algorithms used are Support Vector Machine, Isolation Forest, Neural network-based Autoencoder and Variational Autoencoder models. The two global wildfire indices investigated were the US National Fire Danger Rating System for California and the McArthur Forest Fire Danger Index for Western Australia, using similar features. For the same data sets, the National Fire Danger Rating System and the McArthur Forest Fire Danger Index prediction outcomes were compared with Machine Learning model outcomes. Higher wildfire prediction accuracy was achieved by the One-class models, exceeding the performance of the two wildfire danger indices by at least 20%. The implications of our research findings have the potential to influence both these wildfire indices and state-of-the-art methods in wildfire prediction by proposing alternative ML methods to model the onset of wildfires.

Postnatal depression (PND) is a mood disorder with potentially devastating effects to the individual on many levels. It can affect cognitive functioning, motivation, and self-esteem. The person can socially withdraw from their immediate familial or social circle. It can affect bonding and quality time between the mother and baby. There are many effective therapeutic treatments used for the treatment of PND such as cognitive-behavioural therapy (CBT) and interpersonal psychotherapy (IPT). This study using a single-case study trial with 15 participants investigates the clinical usefulness of combining CBT with virtual reality (VR). Results show that the combination of CBT with VR is an effective treatment for PND. In addition, VR can enhance awareness, decision-making, and self-appreciation within the individual and can also have real-life applications. This study also shows that the combination of VR and CBT is feasible, while the use of such a technology is well accepted.