Welcome to OpenAIR@RGU

Young people responding to multiple "crises": intersecting precarities and everyday life in an informal settlement in Kenya. (2025)
Book Chapter
MUELLER-HIRTH, N. and VERTIGANS, S. 2025. Young people responding to multiple crises: intersecting precarities and everyday life in an informal settlement in Kenya. In Eriksson, S. and Buettgen, A. (eds.) Young people in times of crises: global revelations and social change. Newcastle upon Tyne: Cambridge Scholars Publishing [online], pages 43-68. Available from: https://www.cambridgescholars.com/product/978-1-0364-4711-3

Informal settlements, while being home to about 1 in 8 people worldwide, face many interconnected challenges relating to housing, services, health, employment, ecological vulnerabilities and legal marginalisation. Young people living in informal cont... Read More about Young people responding to multiple "crises": intersecting precarities and everyday life in an informal settlement in Kenya..

Natural XAI: generating feasible, actionable, and causally-aware counterfactual explanations in natural language. (2025)
Presentation / Conference Contribution
SALIMI, P. 2025. Natural XAI: generating feasible, actionable, and causally-aware counterfactual explanations in natural language. In Martin, K. and Ye, X. (eds.) ICCBR-WS 2025: joint proceedings of the workshops and doctoral consortium at the 33rd International conference on case-based reasoning (ICCBR-WS 2025) co-located with the 33rd International conference on case-based reasoning (ICCBR 2025), 30 June 2025, Biarritz, France. CEUR workshop proceedings, 3993. Aachen: CEUR-WS [online], pages 95-99. Available from: https://ceur-ws.org/Vol-3993/short9.pdf

Counterfactual explanations have become a significant component in eXplainable AI (XAI), offering intuitive "what if" scenarios. However, typical numeric or tabular outputs can be vague to non-technical audiences. Additionally, many counterfactual me... Read More about Natural XAI: generating feasible, actionable, and causally-aware counterfactual explanations in natural language..

Leveraging ensemble LLMs and contextual embeddings for case-based reasoning in the legal domain. (2025)
Presentation / Conference Contribution
ABEYRATNE, R. 2025. Leveraging ensemble LLMs and contextual embeddings for case-based reasoning in the legal domain. In Martin, K. and Ye, X. (eds.) ICCBR-WS 2025: joint proceedings of the workshops and doctoral consortium at the 33rd International conference on case-based reasoning (ICCBR-WS 2025) co-located with the 33rd International conference on case-based reasoning (ICCBR 2025), 30 June 2025, Biarritz, France. CEUR workshop proceedings, 3993. Aachen: CEUR-WS [online], pages 68-73. Available from: https://ceur-ws.org/Vol-3993/short1.pdf

This research investigates the integration of Case-Based Reasoning (CBR) with Retrieval-Augmented Generation (RAG) for Large Language Models (LLMs) to enhance the reliability of legal question-answering systems. Thus far, we have developed a structur... Read More about Leveraging ensemble LLMs and contextual embeddings for case-based reasoning in the legal domain..

Agentic CBR in action: empowering loan approvals through interactive, counterfactual explanations. (2025)
Presentation / Conference Contribution
SALIMI, P., WIRATUNGA, N. and CORSAR, D. 2025. Agentic CBR in action: empowering loan approvals through interactive, counterfactual explanations. In Martin, K. and Ye, X. (eds.) ICCBR-WS 2025: joint proceedings of the workshops and doctoral consortium at the 33rd International conference on case-based reasoning (ICCBR-WS 2025) co-located with the 33rd International conference on case-based reasoning (ICCBR 2025), 30 June 2025, Biarritz, France. CEUR workshop proceedings, 3993. Aachen: CEUR-WS [online], pages 27-42. Available from: https://ceur-ws.org/Vol-3993/paper3.pdf

Large Language Models (LLMs) have demonstrated impressive conversational capabilities, yet their susceptibility to hallucinations and inconsistent recommendations poses significant risks in high-stakes domains such as finance. This paper presents an... Read More about Agentic CBR in action: empowering loan approvals through interactive, counterfactual explanations..

Enhancing decision making through similarity-driven knowledge integration in resource allocation and content matching. (2025)
Presentation / Conference Contribution
JAYAWARDENA, L. 2025. Enhancing decision making through similarity-driven knowledge integration in resource allocation and content matching. In Martin, K. and Ye, X. (eds.) ICCBR-WS 2025: joint proceedings of the workshops and doctoral consortium at the 33rd International conference on case-based reasoning (ICCBR-WS 2025) co-located with the 33rd International conference on case-based reasoning (ICCBR 2025), 30 June 2025, Biarritz, France. CEUR workshop proceedings, 3993. Aachen: CEUR-WS [online], pages 90-94. Available from: https://ceur-ws.org/Vol-3993/short7.pdf

This research aims to build a novel framework that enhances decision-making through an integration of similarity driven Case-Based Reasoning (CBR) with advanced Large Language Model (LLM) techniques via Retrieval Augmented Generation (RAG) and Geneti... Read More about Enhancing decision making through similarity-driven knowledge integration in resource allocation and content matching..