Submission of Written Evidence to the House of Lords Communications and Digital Committee Inquiry on The Future of News: Impartiality, Trust, and Technology

I. Technology's Dual Impact on News: Technology and AI democratize information access and innovate news delivery, but also bring challenges like major platform dominance and misinformation, necessitating a balance for a diverse news environment. II. Influence of Major Platforms: The large technology platforms are the key shapers of public opinion and news visibility, these platforms enhance information access yet can overshadow smaller outlets, significantly impacting news dynamics play a key role in shaping public opinion and determining the visibility of news content. III. Generative AI's Role in Media Business: Generative AI’s integration into newsrooms facilitates automated content creation and enhances user-specific content delivery, reshaping journalism's traditional models. However, it also presents challenges like ensuring authenticity and managing AI-generated misinformation, necessitating a balance between technological innovation and ethical journalistic practices. IV. Evolving Perceptions of Impartiality: A growing complexity in maintaining impartiality amidst societal polarization; news outlets must balance unbiased reporting with diverse audience expectations. Media and AI literacy is key to ensuring a well-informed public capable of critically engaging with news in the digital age. V. Effectiveness of Regulatory Oversight: As the media environment becomes increasingly complex with the advent of digital platforms, the role of regulators like Ofcom is crucial but also challenged. This situation calls for a potential reassessment and adaptation of regulatory approaches to address the nuances of modern media consumption and distribution more effectively. VI. Government's Intervention in Media: Government intervention in media can play a pivotal role in ensuring news impartiality and trust, dealing with challenges like the influence of major tech platforms, media plurality, and misinformation. While being vital for a balanced media environment, it is important to preserve journalistic independence and prevent excessive governmental influence, ensuring a healthy, diverse, and independent media landscape. VII. Rumour Control and Influential Nodes: AI literacy and efficient rumour control are key to maintaining news integrity and building trust while addressing impartiality issues. This involves using AI-driven models for rumour management and techniques to identify influential individuals and nodes within social networks that can maximize news virality or trust. These strategies are fundamental in navigating the complexities of information dissemination and control in the digital media landscape

Guarding the UK's Critical Infrastructure: The Rumour Challenge in Cyber Resilience

Written Evidence (CYB0001): House of Lords Communications and Digital Select Committee Inquiry on Cyber resilience of the UK's Critical National Infrastructure

Transforming Society with Natural Language Processing: The Power of Language Technology in Social Impact

I had the privilege of being the guest of honour and keynote speaker at the IEEE Sponsored 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT-2024 ), organized by Chandigarh University (CU), India, 2nd-3rd May, 2024. My keynote, "Transforming Society with Natural Language Processing," showcased how NLP technology has revolutionized communication, accessibility, and understanding across various sectors. United in our mission, we are driving technological breakthroughs in NLP to catalyze significant social change

HumourHindiNet: Humour detection in Hindi web series using word embedding and convolutional neural network

Humour is a crucial aspect of human speech, and it is, therefore, imperative to create a system that can offer such detection. While data regarding humour in English speech is plentiful, the same cannot be said for a low-resource language like Hindi. Through this paper, we introduce two multimodal datasets for humour detection in the Hindi web series. The dataset was collected from over 500 minutes of conversations amongst the characters of the Hindi web series Kota−Factory and Panchayat. Each dialogue is manually annotated as Humour or Non-Humour. Along with presenting a new Hindi language-based Humour detection dataset, we propose an improved framework for detecting humour in Hindi conversations. We start by preprocessing both datasets to obtain uniformity across the dialogues and datasets. The processed dialogues are then passed through the Skip-gram model for generating Hindi word embedding. The generated Hindi word embedding is then passed onto three convolutional neural network (CNN) architectures simultaneously, each having a different filter size for feature extraction. The extracted features are then passed through stacked Long Short-Term Memory (LSTM) layers for further processing and finally classifying the dialogues as Humour or Non-Humour. We conduct intensive experiments on both proposed Hindi datasets and evaluate several standard performance metrics. The performance of our proposed framework was also compared with several baselines and contemporary algorithms for Humour detection. The results demonstrate the effectiveness of our dataset to be used as a standard dataset for Humour detection in the Hindi web series. The proposed model yields an accuracy of 91.79 and 87.32 while an F1 score of 91.64 and 87.04 in percentage for the Kota−Factory and Panchayat datasets, respectively

AI Unveiled Personalities: Profiling Optimistic and Pessimistic Attitudes in Hindi Dataset using Transformer-based Models

Both optimism and pessimism are intricately intertwined with an individual's inherent personality traits and people of all personality types can exhibit a wide range of attitudes and behaviours, including levels of optimism and pessimism. This paper undertakes a comprehensive analysis of optimistic and pessimistic tendencies present within Hindi textual data, employing transformer-based models. The research represents a pioneering effort to define and establish an interaction between the personality and attitude chakras within the realm of human psychology. Introducing an innovative "Chakra" system to illustrate complex interrelationships within human psychology, this work aligns the Myers-Briggs Type Indicator (MBTI) personality traits with optimistic and pessimistic attitudes, enriching our understanding of emotional projection in text. The study employs meticulously fine-tuned transformer models—specifically mBERT, XLM-RoBERTa, IndicBERT, mDeBERTa and a novel stacked mDeBERTa —trained on the novel Hindi dataset ‘मनोभाव’ (pronounced as Manobhav). Remarkably, the proposed Stacked mDeBERTa model outperforms others, recording an accuracy of 0.7785 along with elevated precision, recall, and F1 score values. Notably, its ROC AUC score of 0.7226 underlines its robustness in distinguishing between positive and negative emotional attitudes. The comparative analysis highlights the superiority of the Stacked mDeBERTa model in effectively capturing emotional attitudes in Hindi text

TLP-NEGCN: Temporal Link Prediction via Network Embedding and Graph Convolutional Networks

Temporal link prediction (TLP) is a prominent problem in network analysis that focuses on predicting the existence of future connections or relationships between entities in a dynamic network over time. The predictive capabilities of existing models of TLP are often constrained due to their difficulty in adapting to the changes in dynamic network structures over time. In this article, an improved TLP model, denoted as TLP-NEGCN, is introduced by leveraging network embedding, graph convolutional networks (GCNs), and bidirectional long short-term memory (BiLSTM). This integration provides a robust model of TLP that leverages historical network structures and captures temporal dynamics leading to improved performances. We employ graph embedding with self-clustering (GEMSEC) to create lower dimensional vector representations for all nodes of the network at the initial timestamps. The node embeddings are fed into an iterative training process using GCNs across timestamps in the dataset. This process enhances the node embeddings by capturing the network’s temporal dynamics and integrating neighborhood information. We obtain edge embeddings by concatenating the node embeddings of the end nodes of each edge, encapsulating the information about the relationships between nodes in the network. Subsequently, these edge embeddings are processed through a BiLSTM architecture to forecast upcoming links in the network. The performance of the proposed model is compared against several baselines and contemporary TLP models on various real-life temporal datasets. The obtained results based on various evaluation metrics demonstrate the superiority of the proposed work

Rumour Stance Classification using A Hybrid of Capsule Network and Multi-Layer Perceptron

The accessibility and comfort of using social media have provided an optimal environment for people to expeditiously spread the information they have and sometimes without any knowledge of the authenticity of the information. Consequently, people inspect the stances reflected in the corresponding responses. To discover the certainty of rumour, stances are generally classified into 4 classes: support, deny, query and comment. This paper brings forward a model for the Stance Classification of Rumours on a Twitter dataset which utilizes the newly introduced Capsule Network along with Multilayer Perceptron. The rule-based strategy is used to merge the output of both the networks in a way that utilizes the strength of the two networks. The performance of the proposed model is surpassing the state-of-the-art with regard to the macro average F1-score indicating better results across different sets of classes

Conceptualizing AI Literacy: Educational and Policy Initiatives for a Future-Ready Society

This paper offers a thorough examination of the essential role of Artificial Intelligence (AI) literacy in contemporary society. It investigates the extensive implications of AI across diverse sectors, such as education, healthcare, and media, emphasizing the notable challenges and opportunities that AI technologies present. The paper scrutinizes real-world examples and the dynamic field of AI applications, including generative AI, to highlight the need for a strategic, multi-tiered approach to enhance AI literacy. This strategy includes aspects of educational integration, workforce development, public awareness, ethical AI practices, and ongoing monitoring. It aims to equip individuals with the necessary knowledge and skills for navigating and succeeding in an AI-centric future. This paper is of relevance to educators, policymakers, industry professionals, and the public interested in understanding and leveraging AI technology. Furthermore, it offers insights into the evolving nature of AI, its impact on decision-making processes, and the importance of ethical considerations, making it a valuable resource for those involved in AI development and implementation. The insights provided in this paper contribute to the broader discourse on the societal integration of AI and the development of comprehensive AI literacy programs

AI and Digital Twins Transforming Healthcare IoT

In this age of digital and smart healthcare, cutting-edge technologies are being used to improve operations, patient well-being, life expectancy, and healthcare costs. Digital Twins (DT) have the potential to significantly change these new technologies. DTs could revolutionise digital healthcare delivery with extraordinary creativity. A digital representation of a physical asset that is always its digital twin due to real-time data processing. This paper proposes and builds a DT-based intelligent healthcare system that is aware of its environment. This approach is a great advance for digital healthcare and could improve service delivery. Our most notable contribution is a machine learning-based electrocardiogram (ECG) classifier model for cardiac diagnostics and early problem detection. Our cardiac models predict some situations with exceptional accuracy when applied to different ways. These findings highlight the potential for Digital Twins in healthcare to create intelligent, comprehensive, and scalable Health-Systems that improve patient-physician communication. Our ECG classifier also sets a precedent for using Artificial Intelligence (AI) and Machine Learning (ML) to continually monitor wide range of human body data and identify outliers. ECG data processing has improved significantly using neural network-based algorithms over classic machine learning methods. In conclusion, our work integrates digital twins with cutting-edge AI and machine learning to revolutionise healthcare. Future healthcare will be predictive and improve lives