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Item A Hormetic Approach to the Value-Loading Problem: Preventing the Paperclip Apocalypse(Springer Nature Singapore Pte Ltd, 2025-10-06) Henry NIN; Pedersen M; Williams M; Martin JLB; Donkin LThe value-loading problem is a major obstacle to creating Artificial Intelligence (AI) systems that align with human values and preferences. Central to this problem is the establishment of safe limits for repeatable AI behaviors. We introduce hormetic alignment, a paradigm to regulate the behavioral patterns of AI, grounded in the concept of hormesis, where low frequencies or repetitions of a behavior have beneficial effects, while high frequencies or repetitions are harmful. By modeling behaviors as allostatic opponent processes, we can use either Behavioral Frequency Response Analysis (BFRA) or Behavioral Count Response Analysis (BCRA) to quantify the safe and optimal limits of repeatable behaviors. We demonstrate how hormetic alignment solves the ‘paperclip maximizer’ scenario, a thought experiment where an unregulated AI tasked with making paperclips could end up converting all matter in the universe into paperclips. Our approach may be used to help create an evolving database of ‘values’ based on the hedonic calculus of repeatable behaviors with decreasing marginal utility. Hormetic alignment offers a principled solution to the value-loading problem for repeatable behaviors, augmenting current techniques by adding temporal constraints that reflect the diminishing returns of repeated actions. It further supports weak-to-strong generalization – using weaker models to supervise stronger ones – by providing a scalable value system that enables AI to learn and respect safe behavioral bounds. This paradigm opens new research avenues for developing computational value systems that govern not only single actions but the frequency and count of repeatable behaviors.Item The challenging meet between human and artificial knowledge. A systems-based view of its influences on firms-customers interaction(Emerald Publishing Limited, 2023-12-18) Saviano M; Del Prete M; Mueller J; Caputo FPurpose This paper aims to recall the attention on a key challenge for customer relationship management related to the role of human agents in the management of the “switch point” for ensuring the effectiveness and efficiency in a customer-machine conversation. Design/methodology/approach This study contributes to the discussion about the firms’ approach to artificial intelligence (AI) in frontline interactions under the conceptual umbrella provided by knowledge management studies. Findings This paper provides a theoretical model for clarifying the role of human intelligence (HI) in AI-based frontline interactions by highlighting the relevance of the actors’ subjectivity in the dynamics and perceptions of customer-machine conversations. Originality/value An AI-HI complementarity matrix is proposed in spite of the still dominant replacement view.Item Developing unbiased artificial intelligence in recruitment and selection : a processual framework : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand(Massey University, 2022) Soleimani, MelikaFor several generations, scientists have attempted to build enhanced intelligence into computer systems. Recently, progress in developing and implementing Artificial Intelligence (AI) has quickened. AI is now attracting the attention of business and government leaders as a potential way to optimise decisions and performance across all management levels from operational to strategic. One of the business areas where AI is being used widely is the Recruitment and Selection (R&S) process. However, in spite of this tremendous growth in interest in AI, there is a serious lack of understanding of the potential impact of AI on human life, society and culture. One of the most significant issues is the danger of biases being built into the gathering and analysis of data and subsequent decision-making. Cognitive biases occur in algorithmic models by reflecting the implicit values of the humans involved in defining, coding, collecting, selecting or using data to train the algorithm. The biases can then be self-reinforcing using machine learning, causing AI to engage in ‘biased’ decisions. In order to use AI systems to guide managers in making effective decisions, unbiased AI is required. This study adopted an exploratory and qualitative research design to explore potential biases in the R&S process and how cognitive biases can be mitigated in the development of AI-Recruitment Systems (AIRS). The classic grounded theory was used to guide the study design, data gathering and analysis. Thirty-nine HR managers and AI developers globally were interviewed. The findings empirically represent the development process of AIRS, as well as technical and non-technical techniques in each stage of the process to mitigate cognitive biases. The study contributes to the theory of information system design by explaining the phase of retraining that correlates with continuous mutability in developing AI. AI is developed through retraining the machine learning models as part of the development process, which shows the mutability of the system. The learning process over many training cycles improves the algorithms’ accuracy. This study also extends the knowledge sharing concepts by highlighting the importance of HR managers’ and AI developers’ cross-functional knowledge sharing to mitigate cognitive biases in developing AIRS. Knowledge sharing in developing AIRS can occur in understanding the essential criteria for each job position, preparing datasets for training ML models, testing ML models, and giving feedback, retraining, and improving ML models. Finally, this study contributes to our understanding of the concept of AI transparency by identifying two known cognitive biases similar-to-me bias and stereotype bias in the R&S process that assist in assessing the ML model outcome. In addition, the AIRS process model provides a good understanding of data collection, data preparation and training and retraining the ML model and indicates the role of HR managers and AI developers to mitigate biases and their accountability for AIRS decisions. The development process of unbiased AIRS offers significant implications for the human resource field as well as other fields/industries where AI is used today, such as the education system and insurance services, to mitigate cognitive biases in the development process of AI. In addition, this study provides information about the limitations of AI systems and educates human decision makers (i.e. HR managers) to avoid building biases into their systems in the first place.Item How do accountants remain relevant? : the future of public practice : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Management at Massey University, Manawatū, New Zealand(Massey University, 2019) Ogilvie, Angus StuartSmall accounting practices in New Zealand are slated to come under increasing stress as computerisation impacts their relevance. Artificial intelligence threatens to undermine any monopoly they possess in terms of specialist knowledge. Whilst firms of all sizes will be impacted, smaller practitioners are likely to be especially vulnerable as they tend to have a singular focus on ensuring their clients are compliant with Inland Revenue. Indeed, they commonly refer to this work as ‘compliance’. This involves bookkeeping, annual accounts production and tax returns, all processes that look set to be automated. Professional bodies within the accountancy discipline are stressing the need to move from providing compliance services to offering business advice. The research question asks how accountants remain relevant during a period of unprecedented technological change to the profession. This thesis uses a mixed-method research methodology to understand the current context that the profession operates in and how accountants in practice perceive their future relevance. Institutional Theory, and the concept of scripting, has been employed in the analysis of the research data to analyse how practitioners are actively considering their future in light of technological change. Accountants in practice perceive a positive future for themselves. The way they organise their practices is likely to change substantially with an increased use of technology and the rise of contractors at the expense of the traditional workforce. One thing is likely: we will need fewer accountants in the future.Item Genetic network programming with reinforcement learning and optimal search component : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Sciences at Massey University, Auckland, New Zealand(Massey University, 2019) Alshehri, Mona Abdulrahman MThis thesis presents ways of improving the genetic composition, structure and learning strategies for a graph-based evolutionary algorithm, called Genetic Networking Programming with Reinforcement Learning (GNP-RL), particularly when working with multi-agent and dynamic environments. GNP-RL is an improvement over Genetic Programming, allowing for the concise representation of solutions in terms of a networked graph structure and uses RL to further refine the graph solutions. This work has improved GNP-RL by combining three new techniques: Firstly, it has added a reward and punishment scheme as part of its learning strategy that supports constraint conformance, allowing for a more adaptive training of the agent, so that it can learn how to avoid unwanted situations more effectively. Secondly, an optimal search algorithm has been combined in the GNP-RL core to get an accurate analysis of the exploratory environment. Thirdly, a task prioritization technique has been added to the agent’s learning by giving promotional rewards, so they are trained on how to take priority into account when performing tasks. In this thesis, we applied the improved algorithm to the Tile World benchmarking testbed, which is considered as one of the standard complex problems in this domain, having only a sparse training set. Our experiment results show that the proposed algorithm is superior than the best existing variant of the GNP-RL algorithm [1]. We have achieved 86.66% test accuracy on the standard benchmarking dataset [2]. In addition, we have created another benchmarking dataset, similar in complexity to the one proposed in [1], to test the proposed algorithms further, where it achieved a test accuracy of 96.66%; that is 33.66% more accurate.Item The investigation of non-contact vital signs detection microwave theoretical models and smart sensing systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Department of Mechanical and Electrical Engineering, SF&AT at Massey University, Palmerston North, New Zealand(Massey University, 2020) Nguyen, Thi Phuoc VanNatural disasters, such as floods, landslides and earthquakes, occur frequently around the world. The consequences of such disasters in developing countries tend to be more severe due to the lack of effective life detector systems. Life signs detecting has been an active and challenging research field that has great potential in the applications such as finding human lives under debris and non-invasive diagnosis and health monitoring. There are obvious limitations of conventional devices such as optical or acoustic detectors. The optical equipment requires operation from experts, while the acoustics need a quiet environment. The detectors with the thermal sensors and wireless tracking systems are also insufficient when the "non-line of sight" problem appears. In addition, vital signs information (such as heartbeat and breathing rate) from non-invasive microwave sensors are very important to locate people or predict health conditions in the cases of defense, smart home applications, and baby monitoring. Since NASA proposed the use of microwave radar sensing system for life detecting, research and implementation on sensitive, effective, and economic vital signs sensing systems based on microwave signals have become very active. Until now, most research on life detectors has concentrated on hardware development, signal processing, and development of new algorithms to improve accuracy of vital signs detection. The present study has focused on microwave sensors, studying microwave theoretical models and searching for life detecting, health care and smart home applications. In this research, the antennae systems for vital signs detection, such as breathing rate, were first investigated to validate their performance in a system at different frequencies. The antennae system had an extremely large band width, operating from L band to the X band. Based on the proposed antennae system, models to evaluate the false alarm/detection probabilities of a microwave sensing system were then developed and validated to examine the accuracy of the system in advance. These models are very useful for hardware development of microwave radar sensors. Further investigation into the theoretical models, proposed a novel system that was inspired by the micro bat animal's physical structure. This system showed an enhancement in the accuracy and directional signals of the microwave sensing system. Artificial intelligence was then integrated with the radar sensing system to develop the smart microwave radar sensing system. The machine learning/ deep learning models based on the collected data were developed. The study indicated high accuracy in classifying different types of breathing disorders.Item Synthesized cooperative strategies for intelligent multi-robots in a real-time distributed environment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand(Massey University, 2009) Lin, CaoyunIn the robot soccer domain, real-time response usually curtails the development of more complex Al-based game strategies, path-planning and team cooperation between intelligent agents. In light of this problem, distributing computationally intensive algorithms between several machines to control, coordinate and dynamically assign roles to a team of robots, and allowing them to communicate via a network gives rise to real-time cooperation in a multi-robotic team. This research presents a myriad of algorithms tested on a distributed system platform that allows for cooperating multi- agents in a dynamic environment. The test bed is an extension of a popular robot simulation system in the public domain developed at Carnegie Mellon University, known as TeamBots. A low-level real-time network game protocol using TCP/IP and UDP were incorporated to allow for a conglomeration of multi-agent to communicate and work cohesively as a team. Intelligent agents were defined to take on roles such as game coach agent, vision agent, and soccer player agents. Further, team cooperation is demonstrated by integrating a real-time fuzzy logic-based ball-passing algorithm and a fuzzy logic algorithm for path planning. Keywords Artificial Intelligence, Ball Passing, the coaching system, Collaborative, Distributed Multi-Agent, Fuzzy Logic, Role AssignmentItem Formalization of higher-level intelligence through integration of intelligent tutoring tools : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Systems, Department of Information Systems, Massey University, Palmerston North, New Zealand(Massey University, 2003) Xu, JianIn contrast with a traditional Intelligent Tutoring System (ITS), which attempts to be fairly comprehensive and covers enormous chunks of a discipline's subject matter, a basic Intelligent Tutoring Tool (ITT) (Patel & Kinshuk, 1997) has a narrow focus. It focuses on a single topic or a very small cluster of related topics. An ITT is regarded as a building block of a larger and more comprehensive tutoring system, which is fundamentally similar with the emerging technology "Learning Objects" (LOs) (LTSC, 2000a). While an individual ITT or LO focuses on a single topic or a very small cluster of knowledge, the importance of the automatic integration of interrelated ITTs or LOs is very clear. This integration can extend the scope of an individual ITT or LO, it can guide the user from a simple working model to a complex working model and provide the learner with a rich learning experience, which results in a higher level of learning. This study reviews and analyses the Learning Objects technology, as well as its advantages and difficulties. Especially, the LOs integration mechanisms applied in the existing learning systems are discussed in detail. As a result, a new ITT integration framework is proposed which extends and formalizes the former ITT integration structures (Kinshuk & Patel, 1997, Kinshuk, et al. 2003) in two ways: identifying and organizing ITTs, and describing and networking ITTs. The proposed ITTs integration framework has the following four notions: (1) Ontology, to set up an explicit conceptualisation in a particular domain, (2) Object Design and Sequence Theory, to identify and arrange learning objects in a pedagogical way through the processes of decomposing principled skills, synthesising working models and placing these models on scales of increasing complexity, (3) Metadata, to describe the identified ITTs and their interrelationships in a cross-platform XML format, and (4) Integration Mechanism, to detect and activate the contextual relationship.Item Human emotion recognition using smart sensors : a thesis submitted in fulfilment of the requirements for the degree of Master of Engineering in Electronics and Communication Engineering, School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand, February 2012(Massey University, 2012) Quazi, Muhammad TauseefEmotions play a vital role in people’s everyday life. It is a mental state that does not arise through free will and is often accompanied by physiological changes. Therefore monitoring these changes is important as they are perceptions of emotional changes and can help in identifying matters of concern at an early stage before they become serious. Emotion recognition has become an important subject when it comes to human-machine interaction. Various methods have been used in the past to detect and evaluate human emotions. The most commonly used techniques include the use of textual information, facial expressions, speech, body gestures and physiological signals. In this project we have developed an emotion recognition system based on information provided by the physiological signals. These signals are obtained from a skin temperature sensor, a heart rate sensor, and a skin conductance sensor. The amplified and filtered signals from the sensors are input into the microcontroller where all the processing takes place. The microcontroller wirelessly transmits data to a computer where it is stored for data analyses and feature extraction for emotion recognition. The four basic emotions observed in this project are happy (excited), sad, angry and neutral (relaxed). The data has been collected from healthy individuals, including both male and female, with ages ranging from 18 to 72 years. K-means clustering algorithm has been used to cluster data into four groups (emotions). A graphical user interface (GUI) has been designed to communicate with the hardware as well as display real-time emotion(s) for the monitored period. The developed system has shown an overall emotion recognition rate of 86.25%.Item The role of intelligent machines on the future battlefield, circa 2030 : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Defence and Strategic Studies at Massey University, Palmerston North, New Zealand(Massey University, 2009) Morton, Ben RobertThe application of Intelligent Machine (IM) technology to the battlefield in future has the potential to revolutionise warfare. Converging trends and incentives are propelling this technology towards military applications at an ever increasing rate. This thesis examines the state of IM employment on the battlefield at the year 2030. The methodology employed in undertaking this thesis is the Extrapolation method. It has been utilised to extrapolate a range of technological, social, geo-strategic and military trends, in order to determine the state of affairs regarding intelligent machines at the subject year 2030. Chapter One examines what proportion of modern military ground forces will consist of IMs at the subject year. It assesses factors both driving and obstructing the development and employment of IM technology, and compares these against environmental developments with respect to time. Chapter Two addresses the likely roles in which IMs will be employed on the battlefield. These include present day military functions, as well as possible new roles enabled by specific characteristics of IMs. The chapter also assesses the potential forms that these IMs may take. Chapter Three focuses on the level of autonomy to be granted to battlefield IMs. It analyses the risks and benefits of autonomous control, and also the advantages and disadvantages of the alternative of teleoperation. The level of autonomy will be a defining factor of the IM presence on the battlefield. Chapter Four investigates the potential organisational architectures that may be employed in organising, commanding and controlling IMs. Specifically, centralised, decentralised, and swarm organisation are examined. The advantages and disadvantages of each, as well as necessary enablers are considered in turn. The conclusion provides an aggregated picture of the IM battlefield presence at the year 2030. It surmises the predicted proportion, roles, the level of autonomy, and organisational architecture of IM technology on the battlefield at the subject year of 2030.