In the era of rapid technological advancement, Artificial Intelligence (AI) is a transformative force, permeating diverse facets of society. However, bias concerns have gained prominence as AI systems become integral to decision-making processes. Bias can exert significant and extensive consequences, influencing individuals, groups, and society. The presence of bias in generative AI or machine learning systems can produce content that exhibits discriminating tendencies, perpetuates stereotypes, and contributes to inequalities. Artificial intelligence (AI) systems have the potential to be employed in various contexts that involve sensitive settings, where they are tasked with making significant judgements that can have profound impacts on individuals' lives. Consequently, it is important to establish measures that prevent these decisions from exhibiting discriminating tendencies against specific groups or populations. This exclusive exploration embarks on a comprehensive journey through the nuanced landscape of bias in AI, unravelling its intricate layers to discern different types, pinpoint underlying causes, and illuminate innovative mitigation strategies. Delving deeper, we investigate the roots of bias in AI, revealing a complex interplay of historical legacies, societal imbalances, and algorithmic intricacies. Unravelling the causes involves exploring unintentional reinforcement of existing biases, reliance on incomplete or biased training data, and the potential amplification of disparities when AI systems are deployed in diverse real-world scenarios. Various domains such as text, image, audio, video and more significant advancements in Generative Artificial Intelligence (GAI) were evidenced. Multiple challenges and proliferation of biases occur in different perspectives considered in the study. Against this backdrop, the exploration transitions to a proactive stance, offering a glimpse into cutting-edge mitigation strategies. Diverse and inclusive datasets emerge as a cornerstone, ensuring representative input for AI models. Ethical considerations throughout the development lifecycle and ongoing monitoring mechanisms prove pivotal in mitigating biases that may arise during training or deployment. Technical and non-technical strategies come to the forefront of pursuing fairness and equity in AI. The paper underscores the importance of interdisciplinary collaboration, emphasising that a collective effort spanning developers, ethicists, policymakers, and end-users is paramount for effective bias mitigation. As AI continues its ascent into various spheres of our lives, understanding, acknowledging, and addressing bias becomes an imperative. This exploration seeks to contribute to the discourse, fostering a deeper comprehension of the challenges posed by bias in AI and inspiring a collective commitment to building equitable, trustworthy AI systems for the future.

This survey delves into cross-layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion detection systems and novel strategies such as blockchain integration. This research aims to promote the development of smart cities by enhancing sustainability, security, and efficiency in the industrial and agricultural sectors through the use of IoT, blockchain, AI, and new communication technologies like 5G. In this paper, we provide a comprehensive review and analysis of secure and energy-efficient cross-layer IoT frameworks based on survey of more than 100 published research articles. We highlight the significance of developing IoT security for robust and sustainable connected systems. We discuss multi-layered security approaches and ways to enhance the energy efficiency of resource-constrained devices in IoT networks. Finally, we identify open research issues and future research directions in the emerging field of cross-layer design for secure and energy-efficient IoT networks. In order to improve cybersecurity and efficiency in smart cities, the research also focuses on developing a secure, energy-efficient IoT framework integrating blockchain, artificial intelligence, and quantum-safe cryptography.

Attention mechanisms have been incorporated into many neural network-based natural language processing (NLP) models. They enhance the ability of these models to learn and reason with long input texts. A critical part of such mechanisms is the computation of attention similarity scores between two elements of the texts using a similarity score function. Given that these models have different architectures, it is difficult to comparatively evaluate the effectiveness of different similarity score functions. In this paper, we proposed a baseline model that captures the common components of recurrent neural network-based Question Answering (QA) systems found in the literature. By isolating the attention function, this baseline model allows us to study the effects of different similarity score functions on the performance of such systems. Experimental results show that a trilinear function produced the best results among the commonly used functions. Based on these insights, a new T-trilinear similarity function is proposed which achieved the higher predictive EM and F1 scores than these existing functions. A heatmap visualization of the attention score matrix explains why this T-trilinear function is effective.

This report introduces an Artificial Intelligence (AI) literacy framework developed by a research team from The University of Canterbury, academyEX, and AUT. The Scaffolded AI Literacy (SAIL) framework was developed from a Delphi Study of 17 experts in AI from Aotearoa New Zealand and overseas, with representatives from both education and industry, and cultural experts.The framework has four levels of capability. Three define AI literacy, with an additional level that indicates what further capabilities come beyond AI literacy. Know and Understand AI Use and Apply AI Evaluate and Create AI + Beyond AI Literacy These levels are not age and stage based. Rather, they provide a scaffolded pathway though levels of competency for all learners.At each level, there are six categories of AI literacy The Impacts of AI What AI Is and How It Works Cognitive Skills Applied Skills Social, Cultural, and Ethical Issues Risks and Mitigations In addition to detailing the SAIL framework, this report provides illustrative examples of learning activities that can be applied at the three main levels of AI literacy, taking account of different learner audiences.The report also includes information about an online tool that has been developed to help educators evaluate or design course materials that address level 1 of the framework.

This survey delves into cross layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion detection systems and novel strategies such as blockchain integration. In this paper, we provide a comprehensive review and analysis of secure and energy efficient cross-layer framework for IoT.We highlight the significance of developing IoT security for robust and sustainable connected systems. We discuss multi-layered security approaches and ways to enhance energy efficiency of resource-constrained devices in IoT networks. Finally, we identify open research issues and future research directions in the emerging field ofcross-layer design for secure and energy efficient IoT networks. We expect this study will stimulate further research endeavors to build a secure and energy-efficient framework for next-generation IoT systems.

The widespread adoption of the Internet of Things (IoT) raises significant concerns regarding security and energy efficiency, particularly for low-resource devices. To address these IoT issues, we propose a cross-layer IoT architecture employing machine learning (ML) models and lightweight cryptography. Our proposed solution is based on role-based access control (RBAC), ensuring secure authentication in large-scale IoT deployments while preventing unauthorized access attempts. We integrate layer-specific ML models, such as long short-term memory networks for temporal anomaly detection and decision trees for application-layer validation, along with adaptive speck encryption for the dynamic adjustment of cryptographic overheads. We then introduce a granular RBAC system that incorporates energy-aware policies. The novelty of this work is the proposal of a cross-layer IoT architecture that harmonizes ML-driven security with energy-efficient operations. The performance of the proposed cross-layer system is evaluated by extensive simulations. The results obtained show that the proposed system can reduce false positives up to 32% and enhance system security by preventing unauthorized access up to 95%. We also achieve 30% reduction in power consumption using the proposed lightweight Speck encryption method compared to the traditional advanced encryption standard (AES). By leveraging convolutional neural networks and ML, our approach significantly enhances IoT security and energy efficiency in practical scenarios such as smart cities, homes, and schools.