Chapter 1

The Efficacy of AI Text Detection: A Critical Analysis of Current Technologies

The proliferation of sophisticated large language models (LLMs) has precipitated a crisis in academic and professional integrity, prompting the development of an array of AI detection tools designed to distinguish machine-generated text from human writing. Systems such as Turnitin AI, GPTZero, and Originality.ai purport to identify the statistical hallmarks

Where Does Bias Come From? Exploring Dataset Imbalance, Annotation Bias, and Pre-existing Modelling Choices

Bias in artificial intelligence systems has become a critical concern as these technologies increasingly influence decision-making across domains such as healthcare, criminal justice, and employment. Bias manifests as systematic errors that lead to unfair or discriminatory outcomes, often disproportionately affecting marginalised groups. Understanding the origins of bias is essential for

Misinformation and the Role of Generative AI Models in Its Spread

Misinformation, defined as false or misleading information disseminated regardless of intent, poses significant challenges to societal trust and democratic processes (Wardle & Derakhshan, 2017). Unlike disinformation, which involves deliberate deception, misinformation encompasses a broader spectrum, including unintentional errors, rumours, and misinterpretations. The advent GenAI models, capable of producing human-like text,

Types and Mechanisms of Censorship in Generative AI Systems

Content restriction in generative AI manifests as explicit or implicit censorship. Explicit censorship uses predefined rules to block content like hate speech or illegal material, employing keyword blacklists, pattern-matching, or classifiers (Gillespie 2018). DeepSeek’s models, aligned with Chinese regulations, use real-time filters to block politically sensitive content, such as

Conceptual Contrasts Between Parroting and Hallucination in Language Models

Advancements in artificial intelligence (AI), particularly in natural language processing (NLP), highlight critical distinctions between parroting and hallucination in language models. Parroting refers to AI reproducing or mimicking patterns and phrases from training data without demonstrating understanding or creativity. Hallucination involves generating factually incorrect, implausible, or fabricated outputs, often diverging

The Pre-Training Process: Principles, Methods, and Mechanisms of Language Pattern Acquisition

Pre-training underpins the capabilities of large-scale language models like BERT and GPT, enabling them to capture linguistic patterns from extensive text corpora. This process equips models with versatile language understanding, adaptable through fine-tuning for tasks such as translation or sentiment analysis. The principles, methods, and mechanisms of pre-training reveal how

The Attention Mechanism: The Key to Understanding Linguistic Relationships

The attention mechanism has fundamentally reshaped natural language processing (NLP), enabling models to capture complex linguistic relationships with unprecedented accuracy. Introduced prominently in Vaswani et al. (2017), attention allows models to focus on relevant parts of input sequences, enhancing performance in tasks like machine translation and sentiment analysis. This essay

Fundamentals and Purpose of Natural Language Processing in Artificial Intelligence

Natural Language Processing (NLP) is a pivotal subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language. By enabling machines to understand, interpret, and generate human language, NLP bridges the gap between human communication and computational systems. This essay explores the fundamentals of NLP, its