http://bura.brunel.ac.uk/handle/2438/8630 2026-06-07T19:02:23Z 2026-06-07T19:02:23Z Chambel Vieira, F Sengul, C http://bura.brunel.ac.uk/handle/2438/33378 2026-06-07T10:34:48Z 2026-05-25T00:00:00Z

Title: BiasShield: An AI Browser Extension Against Online Misogyny Authors: Chambel Vieira, F; Sengul, C Abstract: Online spaces frequently expose women to sexualised and objectifying content, with documented harms including body dissatisfaction, anxiety, and depression. Automated moderation algorithms compound this through gendered bias by disproportionately classifying benign images of women as sexualised. Deepfake technologies have intensified the harms, with the victims being predominantly women. To counter these developments, we present BiasShield, a browser extension that identifies, audits, and enables users to manage exposure to misogynistic and deepfake content. We report on the design of a multimodal classifier and evaluate its capacity to detect misogynistic content while reducing gender-based false positives. By making algorithmic bias visible and actionable through exposure analytics and protective measures- including optional blurring of offensive content—BiasShield turns content moderation on the web into informed, user-based control.

2026-05-25T00:00:00Z Rotalinti, Y Ordish, J Liu, X Glocker, B Denniston, A Wright, P Yau, C Kale, A Grainger, D Branson, R Myles, P Tucker, A http://bura.brunel.ac.uk/handle/2438/33361 2026-06-05T02:00:37Z 2026-04-29T00:00:00Z

Title: Identifying and understanding significant change due to drift when assessing AI models in healthcare: a narrative review Authors: Rotalinti, Y; Ordish, J; Liu, X; Glocker, B; Denniston, A; Wright, P; Yau, C; Kale, A; Grainger, D; Branson, R; Myles, P; Tucker, A Abstract: Artificial intelligence (AI) as a Medical Device (AIaMD) or medical devices that use AI algorithms—like any other medical device—must meet the requirements of medical device regulation. For regulatory purposes, the most relevant requirement is that the developer must provide evidence that the device performs as intended under normal conditions of use for its entire lifecycle. However, healthcare data are not static and underlying characteristics can change for many reasons (eg, the introduction of new technologies which improve measurement accuracy, changes in population demographics, etc). This ‘drift’ may lead to a change in performance overall or in certain subgroups in AI models. Models can be updated with new data if significant drift is identified, but in the context of AIaMD, this needs to be done transparently and within a robust regulatory framework. This paper reports on the consensus view of an expert working group hosted by the UK Medicines and Healthcare products Regulatory Agency (MHRA). It aims to highlight the challenges with identifying and assessing significant changes in the performance of a model and understanding the nature of a detected drift to preserve patient safety. We discuss distinct drift subtypes from a statistical perspective and highlight potential causes in the real world that could lead to significant changes to the performance of AI algorithms. We also outline the regulatory challenges associated with risk assessment and the characteristics of drift that are crucial to examine (such as speed and severity) to correctly address interventions and ensure the deployment of safe healthcare products on the market. Finally, we discuss a range of considerations to best identify, risk-assess and intervene for drift when assessing healthcare AI products. Description: ...

2026-04-29T00:00:00Z Nwogu, Chukwudi http://bura.brunel.ac.uk/handle/2438/33356 2026-06-04T02:00:38Z 2025-01-01T00:00:00Z

Title: Towards an Adaptable Architecture for Digital Twin (AADT) Authors: Nwogu, Chukwudi Abstract: A Digital twin (DT) is a virtual replica of a physical object, which has the capability of integrating with a virtual object, such that they exchange data and use the data exchanged to improve each other. It is a confluence of Industry 4.0 technological innovations, such as big data, artificial intelligence, modelling and simulation, Internet of things, optimisation techniques, cyber-physical systems, amongst others; the blend of these technologies fused together for the development of a DT is driven by its use case. A DT has enormous potential, and there is a consensus among the industry, academia and governments that it is one of the most pivotal technologies in Industry 4.0 era that will play a major role in shaping the society. This widely acknowledged notion about DT has not translated into a joint effort to standardise it. As a result, there exists neither a generally accepted definition nor architecture for digital twin. The lack of standard definition, framework or architecture for DT may have a negative impact on the wider adoption and development of DT. The state-of-the-art in DT architecture, for instance, reveals that most of the architectures are designed for specific domains and/or technologies and have components that are named in such a manner that it is difficult to identify commonality in purpose and functionalities. To contribute to the taxonomy of DT architectural components, this study proposes an adaptable architecture for DT (AADT), which is developed based on design science research (DSR) principles. AADT directly addresses the architectural chaos inherited from the growth era of the digital architectural development by establishing standard components traceable to digital twin definitions, requirements, and mandatory functionalities. Rather than proposing yet another domain-specific architecture, AADT provides a systematic process for deriving architectures from requirements, enabling consistent yet flexible implementations. To support the implementation of digital twins, this research develops an implementation framework for digital twin (IFDT) that is a confluence of software development lifecycle and principles of project controls. IFDT consists of a stage gate within every lifecycle phase, which ensures that a digital twin development project is subject to business case viability test and stakeholders’ approval as it progresses from one lifecycle phase to another. AADT is evaluated with the guidance of framework for evaluating design science (FEDS) and ISO Standard 9126. The evaluation of AADT results in the development of a taxonomy of architectural adaptiveness for digital twin systems. The taxonomy organises structural, behavioural, functional and quality adaptiveness into a coherent analytical framework; and therefore, serves as both a conceptual contribution and a practical guide for evaluating future digital twin architectures. In summary, this research, contributes to the taxonomy of digital twin architectural components; develops a technology-agnostic digital twin architecture that can adapt to the requirements of disparate use cases from a wide range of domains; proposes an implementation framework for digital twin; and develops a taxonomy of architectural adaptiveness of digital twin systems. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London

2025-01-01T00:00:00Z Zhou, M Aburumman, N Li, Z Raisamo, R http://bura.brunel.ac.uk/handle/2438/33352 2026-06-02T02:00:33Z 2026-01-01T00:00:00Z

Title: VR-Deform: Real-Time Visual and Haptic Interaction with Deformable Bodies using XPBD in Virtual Reality Authors: Zhou, M; Aburumman, N; Li, Z; Raisamo, R Abstract: ... Description: ...

2026-01-01T00:00:00Z