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While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis; (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering; and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions.

For over two decades now, the application of Citizen Science to Education has been evolving, and fundamental topics, such as the drivers of motivation to participate in Citizen Science projects, are still under discussion. Some recent developments, though, like the use of Artificial Intelligence to support data collection and validation, seem to point to a clear-cut divergence from the mainstream research path. The objective of this paper is to summarise the development trajectory of research on Citizen Science in Education so far, and then shed light on its future development, to help researchers direct their efforts towards the most promising open questions in this field. We achieved these objectives by using the lens of the Affordance-Actualisation theory and the Main Path Analysis method.

Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. KEY POINTS., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

This paper introduces a kaupapa Māori model for the creation of Māori Information Technology (IT) artefacts, an alternative Artificial Intelligence (AI) related development to the exciting colonial dominated AI biased systems. In Aotearoa, Māori are overrepresented in underachievement in education, poor health, welfare dependency and incarceration rates (New Zealand Department of Corrections. 2007. Over-representation of Māori in the criminal justice system: an exploratory report. Department of Corrections [updated January 2022; accessed]. https:// www.corrections.govt.nz/__data/assets/pdf_file/0014/10715/Overrepresentation- of-Maori-in-the-criminal-justice-system.pdf.; Maclaurin J, Liddicoat J, Gavighan C, Knott A, Zerilli J. 2019. Government use of artificial intelligence in New Zealand. Wellington, New Zealand: The New Zealand Law Foundation). These disparities are now surfacing in imperial algorithms and exacerbating biased stereotypes in AI systems. We theorise that Kaupapa Māori theory is the foundation for the action of a Kaupapa Māori Modelling IT Artefact that provides solutions to solve whānau, hapū and iwi problems. We reflected on a critical review of selected literature on historical and contemporary Māori leadership and governance to identify elements of mātauranga and tikanga Māori that could enshrine the IT Artefacts. Investigations then took place to seek ways to transfer these elements of mātauranga and tikanga Māori into framed IT Artefacts during the problem initiation stage of the artefact. This paper presents a kaupapa Māori model for the creation of Māori IT artefacts. Whilst no discrete testing was undertaken, the Kaupapa Māori model provides an avenue to pursue an ontological paradigm using cause and effect theory for future research. [ABSTRACT FROM AUTHOR]

The article highlights the Eighth International Conference on Knowledge Based Intelligent Information and Engineering Systems (KES) held September 2004 at the Intercontinental Hotel in Wellington, Zealand. The event was hosted by the Wellington Institute of Technology. KES 2004 aimed to provide a high-tech forum for the presentation of recent research into the theory and applications of intelligent systems and techniques.

Over the last decade, the Intelligent Computer Tutoring Group (ICTG) has implemented many successful constraint-based Intelligent Tutoring Systems (ITSs) in a variety of instructional domains. Our tutors have proven their effectiveness not only in controlled lab studies but also in real classrooms, and some of them have been commercialized. Although constraint-based tutors seem easier to develop in comparison to other existing ITS methodologies, they still require substantial expertise in Artificial Intelligence (AI) and programming. Our initial approach to making the development easier was WETAS (Web-Enabled Tutor Authoring System), an authoring shell that provided all the necessary functionality for ITSs but still required domain models to be developed manually. This paper presents ASPIRE (Authoring Software Platform for Intelligent Resources in Education), a complete authoring and deployment environment for constraint-based ITSs. ASPIRE consists of the authoring server (ASPIRE-Author), which enables domain experts to easily develop new constraint-based tutors, and a tutoring server (ASPIRE-Tutor), which deploys the developed systems. ASPIRE-Author supports the authoring of the domain model, in which the author is required to provide a high-level description of the domain, as well as examples of problems and their solutions. From this information, ASPIRE generates the domain model automatically. We discuss the authoring process and illustrate it using the development process of CIT, an ITS that teaches capital investment decision making. We also discuss a preliminary study of ASPIRE, and some of the ITSs being developed in it. (Contains 18 figures, 2 footnotes, and 3 tables.)

Proper management of the earth's natural resources is imperative to combat further degradation of the natural environment. However, the environmental datasets necessary for informed resource planning and conservation can be costly to collect and annotate. Consequently, there is a lack of publicly available datasets, particularly annotated image datasets relevant for environmental conservation, that can be used for the evaluation of machine learning algorithms to determine their applicability in real-world scenarios. To address this, the Time-evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) project in New Zealand aims to provide a collection of datasets and accompanying example notebooks for their analysis. This paper showcases three New Zealand-based annotated image datasets that form part of the collection. The first dataset contains annotated images of various predator species, mainly small invasive mammals, taken using low-light camera traps predominantly at night. The second provides aerial photography of the Waikato region in New Zealand, in which stands of Kahikatea (a native New Zealand tree) have been marked up using manual segmentation. The third is a dataset containing orthorectified high-resolution aerial photography, paired with satellite imagery taken by Sentinel-2. Additionally, the TAIAO web platform also contains a collated list of other datasets provided and licensed by our data partners that may be of interest to other researchers. [ABSTRACT FROM AUTHOR]

The judiciary has historically been conservative in its use of Artificial Intelligence, but recent advances in machine learning have prompted scholars to reconsider such use in tasks like sentence prediction. This paper investigates by experimentation the potential use of explainable artificial intelligence for predicting imprisonment sentences in assault cases in New Zealand's courts. We propose a proof-of-concept explainable model and verify in practice that it is fit for purpose, with predicted sentences accurate to within one year. We further analyse the model to understand the most influential phrases in sentence length prediction. We conclude the paper with an evaluative discussion of the future benefits and risks of different ways of using such an AI model in New Zealand's courts. [ABSTRACT FROM AUTHOR]

This paper is devoted to the brilliant Czech logician and philosopher of language Pavel Tichý (1936-1994) who, after emigrating to New Zealand in 1970 and spending half his life there as a political refugee, committed suicide shortly before returning to his alma mater, Charles University in Prague, as Chair of the Department of Logic in the Faculty of Arts. After tracing a biographical profile of the Czech logician, the paper explains some of the central ideas of Tichý's highly original theory, called Transparent Intensional Logic, while locating it in the wider context of the analytic philosophy of language. The paper concludes by highlighting the role played by Tichý's intensional theory in advancing various disciplines, including artificial intelligence, with the aim of shedding light on the significant contributions of the Czech logician, who has yet to gain due recognition. [ABSTRACT FROM AUTHOR]

Artificial intelligence systems require large amounts of data to allow them to learn and achieve high performance. That data is increasingly collected in extractive and exploitative ways, which transfer value and power from individuals to AI system owners. Our research focuses on data that is collected from users of digital platforms, through direct and indirect interaction with those platforms, in ways that are not communicated to users, without consent or compensation. This paper presents our findings from a series of interviews and workshops in the Aotearoa New Zealand context to identify common themes and concerns from a variety of perspectives. Reframing this type of interaction as work or labour brings into view an otherwise unrecognised harm of using this data for training AI systems, and illustrates a new class of exploitative data practices that have become normalised in the digital age. We found that participants particularly emphasised moral or ethical justifications for intervention over financial or economic reasons to act. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

Aquaculture is an important industry in New Zealand (NZ). Mussel farmers often manually check the state of the buoys that are required to support the crop, which is labour-intensive. Artificial intelligence (AI) can provide automatic and intelligent solutions to many problems but has seldom been applied to mussel farming. In this paper, a new AI-based approach is developed to automatically detect buoys from mussel farm images taken from a farm in the South Island of NZ. The overall approach consists of four steps, i.e. data collection and preprocessing, image segmentation, keypoint detection and feature extraction, and classification. A convolutional neural network (CNN) method is applied to perform image segmentation. A new genetic programming (GP) method with a new representation, a new function set and a new terminal set is developed to automatically evolve descriptors for extracting features from keypoints. The new approach is applied to seven subsets and one full dataset containing images of buoys over different backgrounds and compared to three baseline methods. The new approach achieves better performance than the compared methods. Further analysis of the parameters and the evolved solutions provides more insights into the performance of the new approach to buoy detection. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of AI based on the idea that systems can learn from data, identify hidden patterns, and make decisions with little/minimal human intervention. Evolutionary computation is an umbrella of population-based intelligent/learning algorithms inspired by nature, where New Zealand has a good international reputation. This paper provides a review on evolutionary machine learning, i.e. evolutionary computation techniques for major machine learning tasks such as classification, regression and clustering, and emerging topics including combinatorial optimisation, computer vision, deep learning, transfer learning, and ensemble learning. The paper also provides a brief review of evolutionary learning applications, such as supply chain and manufacturing for milk/dairy, wine and seafood industries, which are important to New Zealand. Finally, the paper presents current issues with future perspectives in evolutionary machine learning. [ABSTRACT FROM AUTHOR]

One of the critical steps in achieving successful global environmental AI is to establish benchmark datasets that can be shared and used by the community as research resources to develop practical AI techniques and tools. In 2019, the NZ Government launched its Aquaculture Strategy, which aims to build NZ as a world-leader in sustainable and innovative aquaculture, where AI can play an important role. This special issue aims to highlight recent advances in AI research and developments from the New Zealand community in terms of theory and applications of AI. Artificial Intelligence (AI) is playing an increasingly significant role in various scientific research areas and real-world applications, ranging from AlphaGo design through medical imaging analysis, earthquake prediction to fish species classification, and fruit maturity estimation to online product recommendation. [Extracted from the article]

Remote sensing was used to map the invasion of yellow-flowered legumes on the Central Plateau of New Zealand to inform weed management strategy. The distributions of Cytisus scoparius (broom), Ulex europaeus (gorse) and Lupinus arboreus (tree lupin) were captured with high-resolution RGB photographs of the plants while flowering. The outcomes of herbicide operations to control C. scoparius and U. europaeus over time were also assessed through repeat photography and change mapping. A grid-square sampling tool previously developed by Manaaki Whenua—Landcare Research was used to help transfer data rapidly from photography to maps using manual classification. Artificial intelligence was trialled and ruled out because the number of false positives could not be tolerated. Future actions to protect the natural values and vistas of the Central Plateau from legume invasion were identified. While previous control operations have mostly targeted large, highly visible legume patches, the importance of removing outlying plants to prevent the establishment of new seed banks and slow spread has been underestimated. Outliers not only establish new, large, long-lived seed banks in previously seed-free areas, but they also contribute more to range expansion than larger patches. Our C. scoparius and U. europaeus change mapping confirms and helps to visualise the establishment and expansion of uncontrolled outliers. The power of visualizing weed control strategies through remote sensing has supported recommendations to improve outlier control to achieve long-term, sustainable landscape-scale suppression of invasive legumes. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to (i) validate the Response Evaluation Criteria in PSMA (RECIP 1.0) criteria in a cohort of biochemically recurrent (BCR) prostate cancer (PCa) patients and (ii) determine if this classification could be performed fully automatically using a trained artificial intelligence (AI) model. Methods: One hundred ninety-nine patients were imaged with [68Ga]Ga-PSMA-11 PET/CT once at the time of biochemical recurrence and then a second time a median of 6.0 months later to assess disease progression. Standard-of-care treatments were administered to patients in the interim. Whole-body tumour volume was quantified semi-automatically (TTVman) in all patients and using a novel AI method (TTVAI) in a subset (n = 74, the remainder were used in the training process of the model). Patients were classified as having progressive disease (RECIP-PD), or non-progressive disease (non RECIP-PD). Association of RECIP classifications with patient overall survival (OS) was assessed using the Kaplan-Meier method with the log rank test and univariate Cox regression analysis with derivation of hazard ratios (HRs). Concordance of manual and AI response classifications was evaluated using the Cohen's kappa statistic. Results: Twenty-six patients (26/199 = 13.1%) presented with RECIP-PD according to semi-automated delineations, which was associated with a significantly lower survival probability (log rank p < 0.005) and higher risk of death (HR = 3.78 (1.96–7.28), p < 0.005). Twelve patients (12/74 = 16.2%) presented with RECIP-PD according to AI-based segmentations, which was also associated with a significantly lower survival (log rank p = 0.013) and higher risk of death (HR = 3.75 (1.23–11.47), p = 0.02). Overall, semi-automated and AI-based RECIP classifications were in fair agreement (Cohen's k = 0.31). Conclusion: RECIP 1.0 was demonstrated to be prognostic in a BCR PCa population and is robust to two different segmentation methods, including a novel AI-based method. RECIP 1.0 can be used to assess disease progression in PCa patients with less advanced disease. This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015. [ABSTRACT FROM AUTHOR]

Climate change has the potential to cause a variety of effects on water resources. It is necessary to assess the potential effects of climate change on hydrologic systems to provide the information needed to develop rational management strategies to cope with such change. This paper reports on a case study of the Waimea Plains catchment located in the Tasman region, South Island, New Zealand. Two methods were used to assess the effects of climate change: (1) trend analysis of historic climate and hydrologic data from routine monitoring systems using the Mann-Kendall method; and (2) modelling of projected effects as a result of standard greenhouse gas emissions scenarios. Trend analysis results were mixed. Statistically significant trends were noted for some climate and hydrologic variables but not others. Modelling started with regionally downscaled climate projections based on the IPCC A1B and A2 emissions scenarios. Modelling projections focused on downstream Waimea River flow and groundwater levels for the critical dry period of a record drought year. Both mechanistic computer modelling (MODFLOW) and artificial intelligence modelling were used. Key inputs for this model, such as rainfall recharge, were obtained from artificial intelligence modelling. Artificial intelligence modelling was also applied directly to project stream flow and groundwater levels. Modelling results were similar for both mechanistic and artificial intelligence models. Water usage increased but the decrease in rainfall recharge of groundwater was largely made up by increased stream recharge. The net result was substantial impact on stream flow but only minor effects on groundwater levels. Recommendations from this study include improved routine monitoring of hydrologic variables and expanded modelling efforts in other catchments under a wide variety of hydrologic and climate change conditions. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is undergoing rapid development, meaning that potential risks in application are not able to be fully understood. Multiple international principles and guidance documents have been published to guide the implementation of AI tools in various industries, including healthcare practice. In Aotearoa New Zealand (NZ) we recognised that the challenge went beyond simply adapting existing risk frameworks and governance guidance to our specific health service context and population. We also deemed prioritising the voice of Māori (the indigenous people of Aotearoa NZ) a necessary aspect of honouring Te Tiriti (the Treaty of Waitangi), as well as prioritising the needs of healthcare service users and their families. Here we report on the development and establishment of comprehensive and effective governance over the development and implementation of AI tools within a health service in Aotearoa NZ. The implementation of the framework in practice includes testing with real-world proposals and ongoing iteration and refinement of our processes. [ABSTRACT FROM AUTHOR]

The promise of artificial intelligence has been originally to put technology at the service of people utilizing powerful information processors and 'smart' algorithms to quickly perform time-consuming data analysis. It soon though became apparent that the capacity of artificial intelligence to scrape and analyze big data would be particularly useful in surveillance policies. In the wider areas of migration and asylum management, increasingly sophisticated artificial intelligence tools have been used to register and manage vulnerable populations without much concern about the potential misuses of the data collected and the overall ethical and legal underpinnings of these operations. This article examines three cases in point. The first case investigates the United Nations High Commissioner for Refugees' decision to deploy a biometric matching engine engaging artificial intelligence to make accessing identification documents easier for both refugees and asylum seekers and the states and organizations they interact with. The second case focuses on the New Zealand government's introduction of artificial intelligence to improve border security and streamline immigration. The third case looks at data scraping and biometric recognition tools implemented by the United States government to track (and eventually deport) undocumented migrants. The article first shows how states and international organizations are increasingly turning to artificial intelligence tools to support the implementation of their immigration policies and programs. Subsequently, the article also outlines how even despite well-intentioned efforts, the decision to use artificial intelligence tools to increase efficiency and support the implementation of migration or asylum management policies and programs often involves jeopardizing or altogether sacrificing individuals' human rights, including privacy and security, and raises concerns about vulnerability and transparency. [ABSTRACT FROM AUTHOR]

Background: Digital contact tracing (DCT) is a method of tracing contact relying on tracking systems using artificial intelligence (AI) as an epidemic prevention solution for the COVID-19 pandemic. This review compares the DCT systems such as Bluetooth, Quick Response (QR) code, Global Positioning System (GPS) or Radio Frequency Identification (RFID) technologies that are used in Malaysia, New Zealand and China during early COVID-19 pandemic. Materials and Methods: A review was conducted based on the multiple data sources from existing electronic articles, newspapers, press releases, government documents, reports and web pages that are available online. Result: There are five mobile applications and one Bluetooth device in used by these countries; Malaysia uses MySejahtera app and MyTrace app, New Zealand uses NZ Covid Trace and Bluetooth COVIDCard and China uses a Health Code app that is available in WeChat and Alipay. All apps are available in Apple store, Google Play and App Gallery, except the NZ COVID tracer is not supported by the App Gallery. All 3 countries utilise the QR code as the main DCT with additional features in a centralised data architecture framework. Based on other available databases; MySejahtera has recorded around 74.9% users in Malaysia as at December 2020, NZ CovidTrace logged around 55.1% as at 19th April 2021, and Alipay tracked around 64.3% as at early 2021. . Conclusion: Effective human resources management with digitalisation is crucial in managing pandemics with multi-approach public health interventions which could enhance public health outcomes with less public rejection. Integration of DCT during pandemic is advantageous and valuable for successful disease control. [ABSTRACT FROM AUTHOR]

• Socio-techno-biological systems are becoming more complex and interdependent. • Approaches to systemic risk detection have not kept pace with these developments. • Artificial intelligence (AI) promises to revolutionize systemic risk detection. • Smarter, more integrated approaches are needed to make AI tools work in sync. • Principles for designing competent AI-based Early Warning Systems are presented. Systemic risks are potential trigger events or developments that could undermine the viability of entire networks or systems. Growing complexity in systems make such risks both more likely to occur and more difficult to anticipate. The tools for detecting systemic risk have not kept pace with these challenges; traditional methods are too intermittent, too slow, and too narrow in focus for timely systemic risk detection. However, recent developments in big data analysis and artificial intelligence (AI) have the potential to revolutionize Early Warning Systems (EWSs) for detecting systemic risk. EWSs that are supported by these technologies could provide users with earlier warning signals of a wider range of risks and more up-to-date measures of the fragility of the system against these risks. This area of research is nascent and lacks a robust methodology for designing such EWSs. Addressing this issue, the present paper: 1) identifies the characteristics of competent EWSs; 2) outlines an approach for designing such EWSs; and 3) illustrates the value of this approach, by discussing the conceptual design of an EWS for detecting biosecurity incursions in the New Zealand pastoral industries. [ABSTRACT FROM AUTHOR]

Intelligent machines are machines with microcontroller(s), actuators and sensors embedded and of reprogrammable intelligence. The Mechatronics course at Massey University, New Zealand, teaches students how to design intelligent machines in an integrated manner. The success of the teaching/learning would be achieved in a way that the students are able to apply what they have learnt from various courses to the design of an intelligent machine. To this end, each student is required to do a design project. This paper presents a sample project that deals with real-time implementation of fuzzy logic for machine intelligence on microcomputer. The objectives identified for the project were to modify Rug-Warrior Pro, an autonomous mobile robot platform, to have a long-ranging capability and to implement obstacle avoidance in an unmapped and changing environment as the machine intelligence. The hardware interfacing and software drivers of the sensors are given, and the techniques for coding the membership functions and defuzzification operation of fuzzy logic are discussed. The machine behaviours are formulated by an If-Then rule base that mimics human heuristic, and the resultant program offers an excellent alternative to more common vector-based navigation methods with a fraction of the processing requirements resulting in a fast-responding, reliable application. [ABSTRACT FROM AUTHOR]

Introduction: The Royal Australian and New Zealand College of Radiologists (RANZCR) led the medical community in Australia and New Zealand in considering the impact of machine learning and artificial intelligence (AI) in health care. RANZCR identified that medical leadership was largely absent from these discussions, with a notable absence of activity from governments in the Australasian region up to 2019. The clinical radiology and radiation oncology sectors were considered ripe for the adoption of AI, and this raised a range of concerns about how to ensure the ethical application of AI and to guide its safe and appropriate use in our two specialties.Methods: RANZCR's Artificial Intelligence Committee undertook a landscape review in 2019 anddetermined that AI within clinical radiology and radiation oncology had the potential to grow rapidly and significantly impact the professions. In order to address this, RANZCR drafted ethical principles on the use of AI and standards to guide deployment and engaged in extensive stakeholder consultation to ensure a range of perspectives were received and considered.Results: RANZCR published two key bodies of work: The Ethical Principles of Artificial Intelligence in Medicine, and the Standards of Practice for Artificial Intelligence in Clinical Radiology.Conclusion: RANZCR's publications in this area have established a solid foundation to prepare for the application of AI, however more work is needed. We will continue to assess the evolution of AI and ML within our professions, strive to guide the upskilling of clinical radiologists and radiation oncologists, advocate for appropriate regulation and produce guidance to ensure that patient care is delivered safely. [ABSTRACT FROM AUTHOR]

Australia and New Zealand have been on the artificial intelligence map for years. However, Australia's size and population density make it unlike other western countries. With a land mass similar in size and shape to the U.S. mainland but a population more like that of greater London, its research communities are not as closely integrated as they are in the U.S. and Europe. To provide an overview of artificial intelligence in Australia and New Zealand, the author of this article will offer snapshots of artificial intelligence research throughout the region's institutes and universities and review its industry and conference activities.

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The question of who should own the copyright of a creative work by an artificial intelligence (Al) is as yet largely unanswered. Due to the author's increased distance from the works being created, the granting of copyright protection to AI has not been forthcoming. This article assesses the prevalence of AI- and computer-generated works in which, beyond the initial input, the works produced involve more artificial than human contribution, and the impact this has on authorship. While the main geographical focus of this article is Australia, it makes comparisons with the UK and New Zealand (NZ) to explore how Australian copyright law can be adapted to incorporate positive aspects of UK and NZ law. This article explains how the statutory provisions in both the UK and NZ are undeniably far-sighted in the modern world, in which computer-generated programmes are increasingly used. This article argues that the case-by-case basis is most suitable when deciding the human entity who is the fortunate receiver of copyright protection in works that are essentially made by an advanced non-human entity. [ABSTRACT FROM AUTHOR]

Automated decision-making systems, developed using artificial intelligence and machine learning processes, are being used by companies, organisations and governments with increasing frequency. The purpose of this article is to outline the urgent case for regulating automated decision-making and examine the possible options for regulation. This article will argue that New Zealand's current approach to regulating decision-making is inadequate. It will then analyse art 22 of the European Union's General Data Protection Regulation, concluding that this regime also has significant flaws. Finally, this article will propose an alternative regulatory solution to address the novel challenge posed by automated decision-making. This solution aims to strike a balance between the interests of organisations in capitalising on the benefits of automated decision-making technology and the interests of individuals in ensuring that their right to freedom from discrimination is upheld. [ABSTRACT FROM AUTHOR]

This article describes important possible scenarios in which rapid advances in artificial intelligence (AI) pose multiple risks, including to democracy and for inter-state conflict. In parallel with other countries, New Zealand needs policies to monitor, anticipate and mitigate global catastrophic and existential risks from advanced new technologies. A dedicated policy capacity could translate emerging research and policy options into the New Zealand context. It could also identify how New Zealand could best contribute to global solutions. It is desirable that the potential benefits of AI are realised, while the risks are also mitigated to the greatest extent possible. [ABSTRACT FROM AUTHOR]

Human civilisation faces a range of existential risks, including nuclear war, runaway climate change and superintelligent artificial intelligence run amok. As we show here with calculations for the New Zealand setting, large numbers of currently living and, especially, future people are potentially threatened by existential risks. A just process for resource allocation demands that we consider future generations but also account for solidarity with the present. Here we consider the various ethical and policy issues involved and make a case for further engagement with the New Zealand public to determine societal values towards future lives and their protection. [ABSTRACT FROM AUTHOR]

Estimating groundwater recharge using mathematical models such as water budget or soil water balance method has been proved to be very difficult due to the complex, uncertain multidimensional nature of the process, despite the simplicity of the concept. Artificial Intelligence (AI) techniques have been proposed to deal with this complexity and uncertainty in a similar way to human thinking and reasoning. This study proposed the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model unconfined groundwater recharge using a set of data records from Kaharoa monitoring site in the North Island of New Zealand. Fifty-three data points, comprising a set of input parameters such as rainfall, temperature, sunshine hours, and radiation, for a period of approximately four and a half years, have been used to estimate ground water recharge. The results suggest that the ANFIS model is overall a reliable estimator for groundwater recharge, the correlation coefficient of the model reached 93% using independent data set. The method is easy, flexible and reliable; hence, it is recommended to be used for similar applications. [ABSTRACT FROM AUTHOR]