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, 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,
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
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 emergence of large language models (LLMs) and generative AI applications has ushered in a new era of artificial intelligence capabilities, fundamentally altering the landscape of computational requirements and associated costs. Generative AI systems, built upon transformer architectures and trained on vast datasets, have demonstrated remarkable scalability and adaptability across
A large language model can be defined as a computational model, typically based on deep neural networks, trained on vast datasets of text to perform a wide range of language-related tasks. According to Vaswani et al. (2017), the advent of transformer architectures marked a pivotal shift in NLP, enabling models
Generative AI relies on a specialised branch of machine learning (ML), namely deep learning (DL) algorithms, which employ neural networks to detect and exploit patterns embedded within data. By processing vast volumes of information, these algorithms are capable of synthesising existing knowledge and applying it creatively. As a result, generative
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 and adaptability through fine-tuning for tasks such as translation or sentiment analysis. The principles, methods, and mechanisms of pre-training reveal
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
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. At its core, NLP combines principles from computer
Machine learning (ML), a subset of AI, focuses on algorithms that enable systems to learn patterns from data and make predictions or decisions. Mitchell (1997, 2) provides a foundational definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance
The concept of Artificial Intelligence is characterised by conceptual plurality, reflecting divergent disciplinary perspectives and evolving technological capacities (Bringsjord & Govindarajulu 2024). Whilst numerous definitions have been proposed, there is no universally accepted formulation. Instead, understandings of AI range from attempts to replicate human cognition to approaches centred on rational
