All Outputs (2)

Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method. (2023)
Conference Proceeding
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..

Explainable weather forecasts through an LSTM-CBR twin system. (2023)
Conference Proceeding
PIRIE, C., SURESH, M., SALIMI, P., PALIHAWADANA, C. and NANAYAKKARA, G. 2022. Explainable weather forecasts through an LSTM-CBR twin system. 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 256-260. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_XCBR_Challenge_RGU.pdf

In this paper, we explore two methods for explaining LSTM-based temperature forecasts using previous 14 day progressions of humidity and pressure. First, we propose and evaluate an LSTM-CBR twin system that generates nearest-neighbors that can be vis... Read More about Explainable weather forecasts through an LSTM-CBR twin system..