16 citations · 20 across the 29 of their papers we have counts for
30 papers
Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design
Zeinab Dehghani, Dhavalkumar Thakker, Koorosh Aslansefat +4
Edge Artificial Intelligence (Edge AI) enables the deployment of AI models directly on local edge devices, while such deployments are subject to strict resource constraints, partic…
Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks
Bart Custers, Koorosh Aslansefat
This paper investigates how multi-agent systems (MAS)-based on large language models (LLMs) can support actuarial risk modelling, with a particular focus on uncertainty quantificat…
Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset
Kuniko Paxton, Amila Akagić, Koorosh Aslansefat +2
Recently, the societal implementation of high-performance image classification models has expanded rapidly. While these models require vast amounts of training data to improve perf…
ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
Mohadeseh Mollapour, Koorosh Aslansefat, Zeinab Dehghani +3
Concept-based explainable artificial intelligence (AI) can make model reasoning more human-understandable, but concept-level outputs are not automatically trustworthy. We introduce…
Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering
Louis Donaldson, Connor Walker, Koorosh Aslansefat +1
Trustworthy deployment of Agentic Retrieval-Augmented Generation (RAG) systems requires mechanisms for estimating when multi-stage reasoning pipelines may fail. This paper presents…
Exploring the Impact of Skin Color on Skin Lesion Segmentation
Kuniko Paxton, Medina Kapo, Amila Akagić +3
Skin cancer, particularly melanoma, remains a major cause of morbidity and mortality, making early detection critical. AI-driven dermatology systems often rely on skin lesion segme…
Evaluating a Multi-Agent Voice-Enabled Smart Speaker for Care Homes: A Safety-Focused Framework
Zeinab Dehghani, Rameez Raja Kureshi, Koorosh Aslansefat +6
Artificial intelligence (AI) is increasingly being explored in health and social care to reduce administrative workload and allow staff to spend more time on patient care. This pap…
A Multi-Modal Dataset for Ground Reaction Force Estimation Using Consumer Wearable Sensors
Parvin Ghaffarzadeh, Debarati Chakraborty, Koorosh Aslansefat +2
This Data Descriptor presents a fully open, multi-modal dataset for estimating vertical ground reaction force (vGRF) from consumer-grade Apple Watch sensors with laboratory force p…
EcoFair: Energy-Efficient Inference Routing for Edge AI under Data Degradation
Mostafa Anoosha, Dhavalkumar Thakker, Kuniko Paxton +4
Medical edge-AI systems must operate under a difficult tension: delivering reliable diagnostic inference while running on devices with limited battery capacity, memory, and compute…
HybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classification
Mostafa Anoosha, Zeinab Dehghani, Kuniko Paxton +2
Vertical Federated Learning (VFL) offers a privacy-preserving paradigm for Edge AI scenarios like mobile health diagnostics, where sensitive multimodal data reside on distributed,…
Skewness-Guided Pruning of Multimodal Swin Transformers for Federated Skin Lesion Classification on Edge Devices
Kuniko Paxton, Koorosh Aslansefat, Dhavalkumar Thakker +1
In recent years, high-performance computer vision models have achieved remarkable success in medical imaging, with some skin lesion classification systems even surpassing dermatolo…
Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting
Kuniko Paxton, Zeinab Dehghani, Koorosh Aslansefat +2
Skin color has historically been a focal point of discrimination, yet fairness research in machine learning for medical imaging often relies on coarse subgroup categories, overlook…
Q-SafeML: Safety Assessment of Quantum Machine Learning via Quantum Distance Metrics
Oliver Dunn, Koorosh Aslansefat, Yiannis Papadopoulos
The rise of machine learning in safety-critical systems has paralleled advancements in quantum computing, leading to the emerging field of Quantum Machine Learning (QML). While saf…
RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs
Connor Walker, Koorosh Aslansefat, Mohammad Naveed Akram +1
Accuracy and safety are paramount in Offshore Wind (OSW) maintenance, yet conventional Large Language Models (LLMs) often fail when confronted with highly specialised or unexpected…
Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
Zahra Zehtabi Sabeti Moghaddam, Zeinab Dehghani, Maneeha Rani +4
Generative AI, such as Large Language Models (LLMs), has achieved impressive progress but still produces hallucinations and unverifiable claims, limiting reliability in sensitive d…
Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning
Kuniko Paxton, Koorosh Aslansefat, Dhavalkumar Thakker +2
Recent advances in deep learning have significantly improved the accuracy of skin lesion classification models, supporting medical diagnoses and promoting equitable healthcare. How…
Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML
Kuniko Paxton, Koorosh Aslansefat, Amila Akagić +2
Recent advancements in skin lesion classification models have significantly improved accuracy, with some models even surpassing dermatologists' diagnostic performance. However, in…
Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression
Kuniko Paxton, Koorosh Aslansefat, Dhavalkumar Thakker +1
Fairness is a critical component of Trustworthy AI. In this paper, we focus on Machine Learning (ML) and the performance of model predictions when dealing with skin color. Unlike o…
Incorporating Failure of Machine Learning in Dynamic Probabilistic Safety Assurance
Razieh Arshadizadeh, Mahmoud Asgari, Zeinab Khosravi +2
Machine Learning (ML) models are increasingly integrated into safety-critical systems, such as autonomous vehicle platooning, to enable real-time decision-making. However, their in…
Explaining Large Language Models with gSMILE
Zeinab Dehghani, Mohammed Naveed Akram, Koorosh Aslansefat +2
Large Language Models (LLMs) such as GPT, LLaMA, and Claude achieve remarkable performance in text generation but remain opaque in their decision-making processes, limiting trust a…
Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanations
Seyed Mohammad Ahmadi, Koorosh Aslansefat, Ruben Valcarce-Dineiro +1
In today's world, the significance of explainable AI (XAI) is growing in robotics and point cloud applications, as the lack of transparency in decision-making can pose considerable…
SafeLLM: Domain-Specific Safety Monitoring for Large Language Models: A Case Study of Offshore Wind Maintenance
Connor Walker, Callum Rothon, Koorosh Aslansefat +2
The Offshore Wind (OSW) industry is experiencing significant expansion, resulting in increased Operations \& Maintenance (O\&M) costs. Intelligent alarm systems offer the prospect…
Scope Compliance Uncertainty Estimate
Al-Harith Farhad, Ioannis Sorokos, Mohammed Naveed Akram +2
The zeitgeist of the digital era has been dominated by an expanding integration of Artificial Intelligence~(AI) in a plethora of applications across various domains. With this expa…
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Koorosh Aslansefat, Mojgan Hashemian, Martin Walker +3
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning…