| Title | A Probabilistic Model for Personality Trait Focused Explainability |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Alharbi, Mohammed, Huang, Shihong, Garlan, David |
| Conference Name | Proceedings of the 4th international Workshop on Context-aware, Autonomous and Smart Architecture (CASA 2021), co-located with the 15th European Conference on Software Architecture |
| Date Published | 09/2021 |
| Conference Location | Virtual (Originally Sweden) |
| Keywords | 2021: October, CMU, explainable software |
| Abstract | Explainability refers to the degree to which a software system’s actions or solutions can be understood by humans. Giving humans the right amount of explanation at the right time is an important factor in maximizing the effective collaboration between an adaptive system and humans during interaction. However, explanations come with costs, such as the required time of explanation and humans’ response time. Hence it is not always clear whether explanations will improve overall system utility and, if so, how the system should effectively provide explanation to humans, particularly given that different humans may benefit from different amounts and frequency of explanation. To provide a partial basis for making such decisions, this paper defines a formal framework that incorporates human personality traits as one of the important elements in guiding automated decision- making about the proper amount of explanation that should be given to the human to improve the overall system utility. Specifically, we use probabilistic model analysis to determine how to utilize explanations in an effective way. To illustrate our approach, Grid – a virtual human and system interaction game -- is developed to represent scenarios for human-systems collaboration and to demonstrate how a human’s personality traits can be used as a factor to consider for systems in providing appropriate explanations. |
| Citation Key | node-81237 |
A Probabilistic Model for Personality Trait Focused Explainability