Dr David Corsar's Outputs (4)

Integrating KGs and ontologies with RAG for personalised summarisation in regulatory compliance. (2024)
Presentation / Conference Contribution
ARSHAD, U., CORSAR, D. and NKISI-ORJI, I. 2024. Integrating KGs and ontologies with RAG for personalised summarisation in regulatory compliance. In Martin, K., Salimi, P. and Wijayasekara, V. (eds.) 2024. SICSA REALLM workshop 2024: proceedings of the SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024), 17 October 2024, Aberdeen, UK. CEUR workshop proceedings, 3822. Aachen: CEUR-WS [online], pages 56-61. Available from: https://ceur-ws.org/Vol-3822/short7.pdf

With the growing complexity and increased volumes, regulatory texts are fast becoming a significant challenge for organisations to remain compliant. Traditional ways of summarising legal texts need to be more accommodating of critical, domain-specifi... Read More about Integrating KGs and ontologies with RAG for personalised summarisation in regulatory compliance..

iSee: advancing multi-shot explainable AI using case-based recommendations. (2024)
Presentation / Conference Contribution
WIJEKOON, A., WIRATUNGA, N., CORSAR, D., MARTIN, K., NKISI-ORJI, I., PALIHAWADANA, C., CARO-MARTÍNEZ, M., DÍAZ-AGUDO, B., BRIDGE, D. and LIRET, A. 2024. iSee: advancing multi-shot explainable AI using case-based recommendations. In Endriss, U., Melo, F.S., Bach, K., et al. (eds.) ECAI 2024: proceedings of the 27th European conference on artificial intelligence, co-located with the 13th conference on Prestigious applications of intelligent systems (PAIS 2024), 19–24 October 2024, Santiago de Compostela, Spain. Frontiers in artificial intelligence and applications, 392. Amsterdam: IOS Press [online], pages 4626-4633. Available from: https://doi.org/10.3233/FAIA241057

Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Recent findings suggest that a single explainer may not meet the diverse needs of multiple users in an AI system; indeed, even individual u... Read More about iSee: advancing multi-shot explainable AI using case-based recommendations..

Building personalised XAI experiences through iSee: a case-based reasoning-driven platform. (2024)
Presentation / Conference Contribution
CARO-MARTÍNEZ, M., LIRET, A., DÍAZ-AGUDO, B., RECIO-GARCÍA, J.A., DARIAS, J., WIRATUNGA, N., WIJEKOON, A., MARTIN, K., NKISI-ORJI, I., CORSAR, D., PALIHAWADANA, C., PIRIE, C., BRIDGE, D., PRADEEP, P. and FLEISCH, B. 2024. Building personalised XAI experiences through iSee: a case-based reasoning-driven platform. In Longo, L., Liu, W. and Montavon, G. (eds.) xAI-2024: LB/D/DC: joint proceedings of the xAI 2024 late-breaking work, demos and doctoral consortium, co-located with the 2nd World conference on eXplainable artificial intelligence (xAI 2024), 17-19 July 2024, Valletta, Malta. Aachen: CEUR-WS [online], 3793, pages 313-320. Available from: https://ceur-ws.org/Vol-3793/paper_40.pdf

Nowadays, eXplainable Artificial Intelligence (XAI) is well-known as an important field in Computer Science due to the necessity of understanding the increasing complexity of Artificial Intelligence (AI) systems or algorithms. This is the reason why... Read More about Building personalised XAI experiences through iSee: a case-based reasoning-driven platform..

iSee: a case-based reasoning platform for the design of explanation experiences. (2024)
Journal Article
CARO-MARTÍNEZ, M., RECIO-GARCÍA, J.A., DÍAZ-AGUDO, B., DARIAS, J.M., WIRATUNGA, N., MARTIN, K., WIJEKOON, A., NKISI-ORJI, I., CORSAR, D., PRADEEP, P., BRIDGE, D. and LIRET, A. 2024. iSee: a case-based reasoning platform for the design of explanation experiences. Knowledge-based systems [online], 302, article number 112305. Available from: https://doi.org/10.1016/j.knosys.2024.112305

Explainable Artificial Intelligence (XAI) is an emerging field within Artificial Intelligence (AI) that has provided many methods that enable humans to understand and interpret the outcomes of AI systems. However, deciding on the best explanation app... Read More about iSee: a case-based reasoning platform for the design of explanation experiences..