Models that are learned from real-world data are often biased because the data usedtotrainthemisbiased.

Work todate has highlighted the challenges inmitigating bias within these datasets.

Background Recent developments in artificial intelligence (AI), particularly Large Language Models (LLMs), have shown promise in automating previously unavailable aspects of student writing assessment and providing detailed, individuated feedback. Our previous research demonstrated that AI systems can reliably assess student writing using standardized rubrics, achieving consistency 2 rates of over 99% over five iterations (Heywood & Carrier, 2024). However, while these systems excel at providing holistic assessment using broad categories, their potential to provide detailed, granular feedback about specific writing errors has not yet been fully explored . This study builds upon our earlier work by developing and testing a sophisticated error classification system that can identify, categorize, and describe writing errors at both the word and sentence levels. The system employs a detailed taxonomy of errors based on established linguistic theory in the area of error classification (Corder, 1967, 1975, 1981; Richards, 1971, 1974; James, 1998). The AI analysis is implemented through carefully designed API calls to Claude 3.5 Sonnet in Python. With this enhanced error classification system, the present study analyzes an error ridden dialogue from an infamous text, English as she is spoke (Fonseca et al., 2004). We also provide the results of a review of the AI analysis by a human panel of experts.

Standard Occupational Classifiers (SOC) are systems used to categorize and classify different types of jobs and occupations based on their similarities in terms of job duties, skills, and qualifications. Integrating these facets with Big Data from job advertisement offers the prospect to investigate labour demand that is specific to various occupations. This project investigates the use of recent developments in natural language processing to construct a classifier capable of assigning an occupation code to a given job advertisement. We develop various classifiers for both UK ONS SOC and US O*NET SOC, using different Language Models. We find that an ensemble model, which combines Google BERT and a Neural Network classifier while considering job title, description, and skills, achieved the highest prediction accuracy. Specifically, the ensemble model exhibited a classification accuracy of up to 61% for the lower (or fourth) tier of SOC, and 72% for the third tier of SOC. This model could provide up to date, accurate information on the evolution of the labour market using job advertisements.

Fine urban change segmentation using multi-temporal remote sensing images is essential for understanding human-environment interactions in urban areas.

We propose a simulation-based approach for interrogating classifiers using generative adversarial models in a systematic manner.

The advent of Large Language Models (LLMs) has provided unprecedented capabilities for analyzing unstructured text data. However, deploying these models as reliable, robust, and scalable classifiers in production environments presents significant methodological challenges. Standard fine-tuning approaches can be resource-intensive and often struggle with the dynamic nature of real-world data distributions, which is common in the industry. In this paper, we propose a comprehensive, semi-supervised framework that leverages the zero- and few-shot capabilities of LLMs for building hierarchical text classifiers as a framework for a solution to these industry-wide challenges. Our methodology emphasizes an iterative, human-in-the-loop process that begins with domain knowledge elicitation and progresses through prompt refinement, hierarchical expansion, and multi-faceted validation. We introduce techniques for assessing and mitigating sequence-based biases and outline a protocol for continuous monitoring and adaptation. This framework is designed to bridge the gap between the raw power of LLMs and the practical need for accurate, interpretable, and maintainable classification systems in industry applications.

Medical imaging plays a vital role in early disease diagnosis and monitoring. Specifically, blood microscopy offers valuable insights into blood cell morphology and the detection of hematological disorders. In recent years, deep learning-based automated classification systems have demonstrated high potential in enhancing the accuracy and efficiency of blood image analysis. However, a detailed performance analysis of specific deep learning frameworks appears to be lacking. This paper compares the performance of three popular deep learning frameworks, TensorFlow with Keras, PyTorch, and JAX, in classifying blood cell images from the publicly available BloodMNIST dataset. The study primarily focuses on inference time differences, but also classification performance for different image sizes. The results reveal variations in performance across frameworks, influenced by factors such as image resolution and framework-specific optimizations. Classification accuracy for JAX and PyTorch was comparable to current benchmarks, showcasing the efficiency of these frameworks for medical image classification.

Maltese is a morphologically rich language with a hybrid morphological system which features both concatenative and non-concatenative processes. This paper analyses the impact of this hybridity on the performance of machine learning techniques for morphological labelling and clustering. In particular, we analyse a dataset of morphologically related word clusters to evaluate the difference in results for concatenative and nonconcatenative clusters. We also describe research carried out in morphological labelling, with a particular focus on the verb category. Two evaluations were carried out, one using an unseen dataset, and another one using a gold standard dataset which was manually labelled. The gold standard dataset was split into concatenative and non-concatenative to analyse the difference in results between the two morphological systems.