16 citations · 21 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities
Koorosh Aslansefat, Panagiota Nikolaou, Martin Walker +9
The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a variety of applications. However, safety assurance is a key barrier to widespread usage, especially given…
Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring
Al-Harith Farhad, Ioannis Sorokos, Andreas Schmidt +3
Machine Learning~(ML) has provided promising results in recent years across different applications and domains. However, in many cases, qualities such as reliability or even safety…
StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance Measures
Mohammed Naveed Akram, Akshatha Ambekar, Ioannis Sorokos +2
Reliability estimation of Machine Learning (ML) models is becoming a crucial subject. This is particularly the case when such \mbox{models} are deployed in safety-critical applicat…
Hardening of Artificial Neural Networks for Use in Safety-Critical Applications -- A Mapping Study
Rasmus Adler, Mohammed Naveed Akram, Pascal Bauer +6
Context: Across different domains, Artificial Neural Networks (ANNs) are used more and more in safety-critical applications in which erroneous outputs of such ANN can have catastro…
Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning
Patrik Feth, Mohammed Naveed Akram, René Schuster +1
Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driv…