3 citations · 7 across the 7 of their papers we have counts for
7 papers
iSee: Advancing Multi-Shot Explainable AI Using Case-based Recommendations
Anjana Wijekoon, Nirmalie Wiratunga, David Corsar +7
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 dive…
XEQ Scale for Evaluating XAI Experience Quality
Anjana Wijekoon, Nirmalie Wiratunga, David Corsar +4
Explainable Artificial Intelligence (XAI) aims to improve the transparency of autonomous decision-making through explanations. Recent literature has emphasised users' need for holi…
Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAI
Anjana Wijekoon, David Corsar, Nirmalie Wiratunga +2
The evolution of Explainable Artificial Intelligence (XAI) has emphasised the significance of meeting diverse user needs. The approaches to identifying and addressing these needs m…
Towards Feasible Counterfactual Explanations: A Taxonomy Guided Template-based NLG Method
Pedram Salimi, Nirmalie Wiratunga, David Corsar +1
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…
Behaviour Trees for Creating Conversational Explanation Experiences
Anjana Wijekoon, David Corsar, Nirmalie Wiratunga
This paper presented an XAI system specification and an interactive dialogue model to facilitate the creation of Explanation Experiences (EE). Such specifications combine the knowl…
Clinical Dialogue Transcription Error Correction using Seq2Seq Models
Gayani Nanayakkara, Nirmalie Wiratunga, David Corsar +2
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 shar…
DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods
Nirmalie Wiratunga, Anjana Wijekoon, Ikechukwu Nkisi-Orji +3
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 purpo…