A User-Centric Evaluation to Generate Case-Based Explanations Using Formal Concept Analysis

Jorro-Aragoneses, Jose L.; Caro-Martínez, Marta; Dí­az-Agudo, Belén; Recio-Garcí­a, Juan A.

doi:10.1007/978-3-030-58342-2_13

Cited by 3 publications

(1 citation statement)

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“…CBR has also been used to provide explanatory cases for black-box recommender systems to achieve justification [4,10]. Explanations for such systems can also be created through relations between features (concepts) [11]. However, the quality of explanations for black-box systems in terms of transparency, interpretability and trustworthiness can still be questionable [20].…”

Section: Related Workmentioning

confidence: 99%

Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists

Flogard

Mengshoel

Bach

2021

Case-Based Reasoning Research and Development

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Checklists are used to aid the fulfillment of safety critical activities in a variety of different applications, such as aviation, health care or labour inspections. However, optimizing a checklist for a specific purpose can be challenging. Checklists also need to be trustworthy and user friendly to promote user compliance. With labour inspections as a starting point, we introduce the Checklist Construction Problem. To address the problem, we seek to optimize the content of labour inspection checklists in order to improve the working conditions in every organisation targeted for inspections. To do so, we introduce a hybrid framework called BCBR to construct trustworthy checklists. BCBR is based on case-based reasoning (CBR) and Bayesian inference (BI) and constructs new checklists based on past cases. A key novelty of BCBR is the use of BI for constructing new features in past cases. The augmented past cases are retrieved via CBR to construct new checklists, which ensures justification for the content of the checklists and promotes trust. Experiments suggest that BCBR is more effective than any other baseline we tested, in terms of constructing trustworthy checklists.

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