Factual and Counterfactual Explanation of Fuzzy Information Granules

Stepin, Ilia; Catalá, Alejandro; Pereira-Fariña, Martín; Alonso, José M.

doi:10.1007/978-3-030-64949-4_6

Cited by 10 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This makes the generation of intelligible natural language explanations possible. Similar approaches have been used in [20] for collision risk assessments, and in [24] for explaining fuzzy systems. These works are limited as they do not implement requirements for causes selection from a set of competing causes and do not also implement constraints for generating counterfactuals.…”

Section: Explanation Generation Approachmentioning

confidence: 99%

From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions

Omeiza,

Anjomshoae,

Webb

et al. 2022

Preprint

View full text Add to dashboard Cite

Commentary driving is a technique in which drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a better understanding and awareness of their surroundings. In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions, and thereby assist a driver or an end-user during driving operations in challenging and safety-critical scenarios. In this paper, we conducted a field study in which we deployed a research vehicle in an urban environment to obtain data. While collecting sensor data of the vehicle's surroundings, we obtained driving commentary from a driving instructor using the think-aloud protocol. We analysed the driving commentary and uncovered an explanation style; the driver first announces his observations, announces his plans, and then makes general remarks. He also made counterfactual comments. We successfully demonstrated how factual and counterfactual natural language explanations that follow this style could be automatically generated using a simple tree-based approach. Generated explanations for longitudinal actions (e.g., stop and move) were deemed more intelligible and plausible by human judges compared to lateral actions, such as lane changes. We discussed how our approach can be built on in the future to realise more robust and effective explainability for driver assistance as well as partial and conditional automation of driving functions.

show abstract

Section: Explanation Generation Approachmentioning

confidence: 99%

From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions

Omeiza,

Anjomshoae,

Webb

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, it is worth noting that explainable fuzzy systems [9] can pave the way from interpretable fuzzy systems to XAI systems. They provide users with both local and global but also intrinsic and extrinsic factual and counterfactual explanations in natural language [20]. They have already been successfully used for generating post-hoc explanations of black-box models [21].…”

Section: B Explainable Models For Fat-ementioning

confidence: 99%

A Framework for Analyzing Fairness, Accountability, Transparency and Ethics: A Use-case in Banking Services

Mariotti

Alonso

Confalonieri

2021

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Self Cite

View full text Add to dashboard Cite

We introduce a novel framework to deal with fairness, accountability and explainability of intelligent systems. This framework puts together several tools to deal with bias at the level of data, algorithms and human cognition. The framework makes use of intelligent classifiers endowed with fuzzy-grounded linguistic explainability. As a result, it facilitates the exhaustive comparison of (white/grey/black)-box modelling techniques in combination with different strategies for handling missing values and unbalanced datasets. The proposal is evaluated on a realworld dataset in the context of banking services and reported results are encouraging.

show abstract

“…Therefore, the loan applicant should be provided with an explanation of the factors involved in the classification and suggestions about how to change an unfavourable outcome. These two requirements are fulfilled with factual and counterfactual explanations in the field of Explainable Artificial Intelligence (Explainable AI or XAI 1 for short) [2,10,35,37,38]. Factuals refer to what is observed in the actual sce-nario (e.g.…”

Section: Introductionmentioning

confidence: 99%

Introducing User Feedback-Based Counterfactual Explanations (UFCE)

Suffian,

Alonso-Moral,

Bogliolo

2024

Int J Comput Intell Syst

View full text Add to dashboard Cite

Machine learning models are widely used in real-world applications. However, their complexity makes it often challenging to interpret the rationale behind their decisions. Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in eXplainable Artificial Intelligence (XAI). CE provides actionable information to users on how to achieve the desired outcome with minimal modifications to the input. However, current CE algorithms usually operate within the entire feature space when optimising changes to turn over an undesired outcome, overlooking the identification of key contributors to the outcome and disregarding the practicality of the suggested changes. In this study, we introduce a novel methodology, that is named as user feedback-based counterfactual explanation (UFCE), which addresses these limitations and aims to bolster confidence in the provided explanations. UFCE allows for the inclusion of user constraints to determine the smallest modifications in the subset of actionable features while considering feature dependence, and evaluates the practicality of suggested changes using benchmark evaluation metrics. We conducted three experiments with five datasets, demonstrating that UFCE outperforms two well-known CE methods in terms of proximity, sparsity, and feasibility. Reported results indicate that user constraints influence the generation of feasible CEs.

show abstract

Factual and Counterfactual Explanation of Fuzzy Information Granules

Cited by 10 publications

References 36 publications

From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions

From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions

A Framework for Analyzing Fairness, Accountability, Transparency and Ethics: A Use-case in Banking Services

Introducing User Feedback-Based Counterfactual Explanations (UFCE)

Contact Info

Product

Resources

About