Outputs (9)

Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method. (2023)
Presentation / Conference Contribution
SALIMI, P., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2023. Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method. In Gal, K., Nowé, A., Nalepa, G.J., Fairstein, R. and Rădulescu, R. (eds.) ECAI 2023: proceedings of the 26th European conference on artificial intelligence (ECAI 2023), 30 September - 4 October 2023, Kraków, Poland. Frontiers in artificial intelligence and applications, 372. Amsterdam: IOS Press [online], pages 2057-2064. Available from: https://doi.org/10.3233/FAIA230499

Counterfactual Explanations (cf-XAI) describe the smallest changes in feature values necessary to change an outcome from one class to another. However, many cf-XAI methods neglect the feasibility of those changes. In this paper, we introduce a novel... Read More about Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method..

Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation. (2023)
Presentation / Conference Contribution
OFORI-BOATENG, R., ACEVES-MARTINS, M., JAYNE, C., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2023. Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation. Porcedia computer science [online], 222: selected papers from the 2023 International Neural Network Society workshop on deep learning innovations and applications (INNS DLIA 2023), co-located with the 2023 International joint conference on neural networks (IJCNN), 18-23 June 2023, Gold Coast, Australia, pages 114-126. Available from: https://doi.org/10.1016/j.procs.2023.08.149

Systematic Review (SR) presents the highest form of evidence in research for decision and policy-making. Nonetheless, the structured steps involved in carrying out SRs make it demanding for reviewers. Many studies have projected the abstract screenin... Read More about Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation..

CBR driven interactive explainable AI. (2023)
Presentation / Conference Contribution
WIJEKOON, A., WIRATUNGA, N., MARTIN, K., CORSAR, D., NKISI-ORJI, I., PALIHAWADANA, C., BRIDGE, D., PRADEEP, P., AGUDO, B.D. and CARO-MARTÍNEZ, M. 2023. CBR driven interactive explainable AI. In MASSIE, S. and CHAKRABORTI, S. (eds.) 2023. Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning 2023, (ICCBR 2023), 17-20 July 2023, Aberdeen, UK. Lecture notes in computer science (LNCS), 14141. Cham: Springer [online], pages169-184. Available from: https://doi.org/10.1007/978-3-031-40177-0_11

Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Numerous explanation techniques (explainers) exist in the literature, and recent findings suggest that addressing multiple user needs requi... Read More about CBR driven interactive explainable AI..

Failure-driven transformational case reuse of explanation strategies in CloodCBR. (2023)
Presentation / Conference Contribution
NKISI-ORJI, I., PALIHAWADANA, C., WIRATUNGA, N., WIJEKOON, A. and CORSAR, D. 2023. Failure-driven transformational case reuse of explanation strategies in CloodCBR. In Massie, S. and Chakraborti, S. (eds.) Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning 2023 (ICCBR 2023), 17-20 July 2023, Aberdeen, UK. Lecture notes in computer science (LNCS), 14141. Cham: Springer [online], pages 279-293. Available from: https://doi.org/10.1007/978-3-031-40177-0_18

In this paper, we propose a novel approach to improve problem-solving efficiency through the reuse of case solutions. Specifically, we introduce the concept of failure-driven transformational case reuse of explanation strategies, which involves trans... Read More about Failure-driven transformational case reuse of explanation strategies in CloodCBR..

AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics. (2023)
Presentation / Conference Contribution
PIRIE, C., WIRATUNGA, N., WIJEKOON, A. and MORENO-GARCIA, C.F. 2023. AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics. In Malburg, L. and Verma, D. (eds.) Workshop proceedings of the 31st International conference on case-based reasoning (ICCBR-WS 2023), 17 July 2023, Aberdeen, UK. CEUR workshop proceedings, 3438. Aachen: CEUR-WS [online], pages 184-199. Available from: https://ceur-ws.org/Vol-3438/paper_14.pdf

As deep learning models become increasingly complex, practitioners are relying more on post hoc explanation methods to understand the decisions of black-box learners. However, there is growing concern about the reliability of feature attribution expl... Read More about AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics..

Machine learning for risk stratification of diabetic foot ulcers using biomarkers. (2023)
Presentation / Conference Contribution
MARTIN, K., UPADHYAY, A., WIJEKOON, A., WIRATUNGA, N. and MASSIE, S. 2023. Machine learning for risk stratification of diabetic foot ulcers using biomarkers. In Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational science: proceedings of the 23rd International conference on computational science 2023 (ICCS 2023): computing at the cutting edge of science (ICCS 2023), 3-5 July 2023, Prague, Czech Republic: [virtual event]. Lecture notes in computer science, 14075. Cham: Springer [online], part III, pages 153-161. Available from: https://doi.org/10.1007/978-3-031-36024-4_11

Development of a Diabetic Foot Ulcer (DFU) causes a sharp decline in a patient's health and quality of life. The process of risk stratification is crucial for informing the care that a patient should receive to help manage their Diabetes before an ul... Read More about Machine learning for risk stratification of diabetic foot ulcers using biomarkers..

Introducing Clood CBR: a cloud based CBR framework. (2023)
Presentation / Conference Contribution
PALIHAWADANA, C., NKISI-ORJI, I., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2022. Introducing Clood CBR: a cloud based CBR framework. In Reuss, P. and Schönborn, J. (eds.) Workshop proceedings of the 30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3389. Aachen: CEUR-WS [online], pages 233-234. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_108.pdf

CBR applications have been deployed in a wide range of sectors, from pharmaceuticals; to defence and aerospace to IoT and transportation, to poetry and music generation; for example. However, a majority of applications have been built using monolithi... Read More about Introducing Clood CBR: a cloud based CBR framework..

iSee: intelligent sharing of explanation experiences. (2023)
Presentation / Conference Contribution
MARTIN, K., WIJEKOON, A., WIRATUNGA, N., PALIHAWADANA, C., NKISI-ORJI, I., CORSAR, D., DÍAZ-AGUDO, B., RECIO-GARCÍA, J.A., CARO-MARTÍNEZ, M., BRIDGE, D., PRADEEP, P., LIRET, A. and FLEISCH, B. 2022. iSee: intelligent sharing of explanation experiences. In Reuss, P. and Schönborn, J. (eds.) Workshop proceedings of the 30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3389. Aachen: CEUR-WS [online], pages 231-232. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_83.pdf

The right to an explanation of the decision reached by a machine learning (ML) model is now an EU regulation. However, different system stakeholders may have different background knowledge, competencies and goals, thus requiring different kinds of ex... Read More about iSee: intelligent sharing of explanation experiences..

iSee: intelligent sharing of explanation experience of users for users. (2023)
Presentation / Conference Contribution
WIJEKOON, A., WIRATUNGA, N., PALIHAWADANA, C., NKISI-ORJI, I., CORSAR, D. and MARTIN, K. 2023. iSee: intelligent sharing of explanation experience of users for users. In IUI '23 companion: companion proceedings of the 28th Intelligent user interfaces international conference 2023 (IUI 2023), 27-31 March 2023, Sydney, Australia. New York: ACM [online], pages 79-82. 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: intelligent sharing of explanation experience of users for users..