iSee: intelligence sharing of explanation experience of users for users. (2022)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., PALIHAWADANA, C., NKISI-ORJI, I., CORSAR, D. and MARTIN, K. [2023]. iSee: intelligence sharing of explanation experience of users for users. To be made available in the Proceedings of the 28th Intelligent user interfaces annual conference 2023 (IUI 2023), 27-31 March 2023, Sydney, Australia. New York: ACM [online], (accepted). To be made available from: https://doi.org/10.1145/3581754.3584137

The right to obtain an explanation of the decision reached by an Artificial Intelligence (AI) model is now an EU regulation. Different stakeholders of an AI system (e.g. managers, developers, auditors, etc.) may have different background knowledge, c... Read More about iSee: intelligence sharing of explanation experience of users for users..

Job assignment problem and traveling salesman problem: a linked optimisation problem. (2022)
Conference Proceeding
OGUNSEMI, A., MCCALL, J., KERN, M., LACROIX, B., CORSAR, D. and OWUSU, G. 2022. Job assignment problem and traveling salesman problem: a linked optimisation problem. In Bramer, M. and Stahl, F (eds.) Artificial intelligence XXXIX: proceedings of the 42nd SGAI (Specialist Group on Artificial Intelligence) Artificial intelligence international conference 2022 (AI 2022), 13-15 December 2022, Cambridge, UK. Lecture notes in computer science (LNCS), 13652. Cham: Springer [online], pages 19-33. Available from: https://doi.org/10.1007/978-3-031-21441-7_2

Linked decision-making in service management systems has attracted strong adoption of optimisation algorithms. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems. This paper, theref... Read More about Job assignment problem and traveling salesman problem: a linked optimisation problem..

Clinical dialogue transcription error correction using Seq2Seq models. (2022)
Conference Proceeding
NANAYAKKARA, G., WIRATURNGA, N., CORSAR, D., MARTIN, K. and WIJEKOON, A. 2022. Clinical dialogue transcription error correction using Seq2Seq models. In Shaban-Nejad, A., Michalowski, M. and Bianco, S. (eds.) Multimodal AI in healthcare: a paradigm shift in health intelligence; selected papers from the 6th International workshop on health intelligence (W3PHIAI-22), co-located with the 34th AAAI (Association for the Advancement of Artificial Intelligence) Innovative applications of artificial intelligence (IAAI-22), 28 February - 1 March 2022, [virtual event]. Studies in computational intelligence, 1060. Cham: Springer [online], pages 41-57. Available from: https://doi.org/10.1007/978-3-031-14771-5_4

Good communication is critical to good healthcare. Clinical dialogue is a conversation between health practitioners and their patients, with the explicit goal of obtaining and sharing medical information. This information contributes to medical decis... Read More about Clinical dialogue transcription error correction using Seq2Seq models..

How close is too close? Role of feature attributions in discovering counterfactual explanations. (2022)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., NKISI-ORJI, I., PALIHAWADANA, C., CORSAR, D. and MARTIN, K. 2022. How close is too close? Role of feature attributions in discovering counterfactual explanations. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 33-47. Available from: https://doi.org/10.1007/978-3-031-14923-8_3

Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, counterfactuals are "actionable" explanations that help users to understand how model decisions can be changed by adapting features of an input. A cas... Read More about How close is too close? Role of feature attributions in discovering counterfactual explanations..

Adapting semantic similarity methods for case-based reasoning in the Cloud. (2022)
Conference Proceeding
NKISI-ORJI, I., PALIHAWADANA, C., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2022. Adapting semantic similarity methods for case-based reasoning in the Cloud. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 125-139. Available from: https://doi.org/10.1007/978-3-031-14923-8_9

CLOOD is a cloud-based CBR framework based on a microservices architecture, which facilitates the design and deployment of case-based reasoning applications of various sizes. This paper presents advances to the similarity module of CLOOD through the... Read More about Adapting semantic similarity methods for case-based reasoning in the Cloud..

Facility location problem and permutation flow shop scheduling problem: a linked optimisation problem. (2022)
Conference Proceeding
OGUNSEMI, A., MCCALL, J., KERN, M., LACROIX, B., CORSAR, D. and OWUSU, G. 2022. Facility location problem and permutation flow shop scheduling problem: a linked optimisation problem. In Fieldsend, J. (ed.) GECCO'22 companion: proceedings of 2022 Genetic and evolutionary computation conference companion, 9-13 July 2022, Boston, USA, [virtual event]. New York: ACM [online], pages 735-738. Available from: https://doi.org/10.1145/3520304.3529033

There is a growing literature spanning several research communities that studies multiple optimisation problems whose solutions interact, thereby leading researchers to consider suitable approaches to joint solution. Real-world problems, like supply... Read More about Facility location problem and permutation flow shop scheduling problem: a linked optimisation problem..

Clinical dialogue transcription error correction using Seq2Seq models. (2022)
Working Paper
NANAYAKKARA, G., WIRATUNGA, N., CORSAR, D., MARTIN, K. and WIJEKOON, A. 2022. Clinical dialogue transcription error correction using Seq2Seq models. arXiv [online]. Available from: https://doi.org/10.48550/arXiv.2205.13572

Good communication is critical to good healthcare. Clinical dialogue is a conversation between health practitioners and their patients, with the explicit goal of obtaining and sharing medical information. This information contributes to medical decis... Read More about Clinical dialogue transcription error correction using Seq2Seq models..

Demystifying the black box: the importance of interpretability of predictive models in neurocritical care. (2022)
Journal Article
MOSS, L., CORSAR, D., SHAW, M., PIPER, I. and HAWTHORNE, C. 2022. Demystifying the black box: the importance of interpretability of predictive models in neurocritical care. Neurocritical care [online], 37(Supplement 2): big data in neurocritical care, pages 185-191. Available from: https://doi.org/10.1007/s12028-022-01504-4

Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neuro... Read More about Demystifying the black box: the importance of interpretability of predictive models in neurocritical care..

DisCERN: discovering counterfactual explanations using relevance features from neighbourhoods. (2021)
Conference Proceeding
WIRATUNGA, N., WIJEKOON, A., NKISI-ORJI, I., MARTIN, K., PALIHAWADANA, C. and CORSAR, D. 2021. DisCERN: discovering counterfactual explanations using relevance features from neighbourhoods. In Proceedings of 33rd IEEE (Institute of Electrical and Electronics Engineers) International conference on tools with artificial intelligence 2021 (ICTAI 2021), 1-3 November 2021, Washington, USA [virtual conference]. Piscataway: IEEE [online], pages 1466-1473. Available from: https://doi.org/10.1109/ICTAI52525.2021.00233

Counterfactual explanations focus on 'actionable knowledge' to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies tha... Read More about DisCERN: discovering counterfactual explanations using relevance features from neighbourhoods..

Actionable feature discovery in counterfactuals using feature relevance explainers. (2021)
Conference Proceeding
WIRATUNGA, N., WIJEKOON, A., NKISI-ORJI, I., MARTIN, K., PALIHAWADANA, C. and CORSAR, D. 2021. Actionable feature discovery in counterfactuals using feature relevance explainers. In Borck, H., Eisenstadt, V., Sánchez-Ruiz, A. and Floyd, M. (eds.) ICCBR 2021 workshop proceedings (ICCBR-WS 2021): workshop proceedings for the 29th International conference on case-based reasoning co-located with the 29th International conference on case-case based reasoning (ICCBR 2021), 13-16 September 2021, Salamanca, Spain [virtual conference]. CEUR-WS proceedings, 3017. Aachen: CEUR-WS [online], pages 63-74. Available from: http://ceur-ws.org/Vol-3017/101.pdf

Counterfactual explanations focus on 'actionable knowledge' to help end-users understand how a Machine Learning model outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to be able to reason with si... Read More about Actionable feature discovery in counterfactuals using feature relevance explainers..