[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81803-en":3,"doc-seo-81803-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81803,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations","Counterfactual explanations are used to clarify machine learning decisions by specifying minimal input changes that would flip a model’s prediction. Many existing approaches generate alternatives that alter outcomes but remain unrealistic because they do not encode domain knowledge and intervention constraints. PACE introduces a modular neuro-symbolic framework that separates neural prediction from symbolic reasoning, enforcing feasible, domain-specific constraints during counterfactual generation. Model-agnostic experiments on Adult Income combine an MLP with ASP rules to balance validity and plausibility.","PACE: A NEURO-SYMBOLIC FRAMEWORK FOR PLAUSIBLE AND ACTIONABLE COUNTERFACTUAL EXPLANATIONS  \narXiv :2607 .0 1306v 1 [ cs .AI] 1 Jul 2026  \nPavel Iakovets 1 ∗ Liyanapathiranage Sudeepika Wajirakumari Samarathunga2 Martin Thomas Horsch2  \nFadi Al Machot2  \n1University of Klagenfurt, Universitätsstraße 65/67, 9020 Klagenfurt am Wörthersee, Austria  \n2Norwegian University of Life Sciences, Elizabeth Stephansens v. 15, 1433 Ås, Norway  \nABSTRACT  \nCounterfactual explanations have emerged as a practical approach for explaining machine learning predictions by identifying minimal changes to an input instance that would alter a model’s decision.  \nAlthough many existing methods successfully generate prediction-changing alternatives, they often produce unrealistic or infeasible recommendations because they lack explicit mechanisms for incorporating domain knowledge and intervention constraints. Neuro-symbolic artificial intelligence offers a promising direction by combining data-driven predictive models with symbolic reasoning capable of representing human-understandable rules and feasible actions. This paper presents PACE, a modular neuro-symbolic framework for generating feasibility-aware counterfactual explanations.  \nThe framework separates prediction and reasoning into two complementary components: a neural predictive model responsible for classification and a symbolic reasoning layer that enforces domainspecific constraints during counterfactual generation. By explicitly modeling feasible interventions, the framework aims to produce explanations that are more consistent with domain knowledge while remaining interpretable and actionable. The proposed approach is model-agnostic and can be adapted to application domains where realistic decision support is required. To demonstrate the applicability of the framework, a case study is conducted on the Adult Income dataset. A multilayer perceptronclassifier is combined with Answer Set Programming (ASP) rules that encode feasible modifications to education, occupation, and working hours while preserving immutable attributes. Experimental results highlight the trade-off between counterfactual validity and plausibility and show that incorporating symbolic constraints yields explanations that better satisfy domain-specific feasibility requirements. These findings illustrate the potential of neuro-symbolic methods for supporting the generation of transparent, feasibility-aware counterfactual explanations in explainable AI systems.  \n1 Introduction  \nThe increasing use of machine learning models in decision-support systems has intensified the demand for explanations that are transparent, actionable, and trustworthy [1, 2, 3] . Among the many approaches proposed in Explainable Artificial Intelligence (XAI), counterfactual explanations have attracted particular attention because they provide actionable recommendations by identifying how an instance could be modified to obtain a different prediction [4] . Such explanations are especially valuable in domains such as healthcare, finance, education, and public administration, where decisions directly affect individuals.  \nDespite their appeal, generating useful counterfactual explanations remains challenging. A desirable counterfactual should be valid, minimal, and plausible. However, many existing methods primarily optimize for prediction change and proximity, often producing recommendations that violate domain constraints or cannot realistically be implemented [5] . As a result, there is growing interest in approaches that incorporate domain knowledge directly into the explanation process, enabling explanations that are not only valid but also feasible in practice.  \n∗ Corresponding author: [paiakovets@edu.aau.at](paiakovets@edu.aau.at)  \nRunning Title for Header  \nNeuro-Symbolic Artificial Intelligence (NeSy) provides a promising foundation for addressing this challenge by combining the representation-learning capabilities of neural networks with t","cbCaiaB8MHeYpN5D","https://ap.wps.com/l/cbCaiaB8MHeYpN5D","pdf",476149,3,1,11,"English","en",105,"# Introduction\n## Counterfactual explanations in XAI\n## Challenges and need for feasibility\n## Neuro-symbolic foundations\n## PACE framework overview\n## Case study and comparisons","[{\"question\":\"What problem does PACE address in counterfactual explanations?\",\"answer\":\"PACE targets the gap between prediction-changing counterfactuals and recommendations that are realistic or implementable. It does so by enforcing domain knowledge and feasible intervention constraints during generation.\"},{\"question\":\"How does the PACE framework combine neural and symbolic components?\",\"answer\":\"PACE uses a neural predictive model for classification and a symbolic reasoning layer to enforce domain-specific constraints. The symbolic layer constrains which interventions are admissible when generating counterfactuals.\"},{\"question\":\"What is the experimental setup for evaluating PACE?\",\"answer\":\"The case study uses the Adult Income dataset, combining an MLP classifier with Answer Set Programming (ASP) rules. The ASP rules encode feasible modifications to education, occupation, and working hours while preserving immutable attributes.\"}]",1784176258,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"pace-a-neuro-symbolic-framework-for-plausible-and-actionable-counterfactual-explanations","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/pace-a-neuro-symbolic-framework-for-plausible-and-actionable-counterfactual-explanations/81803/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-23","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does PACE address in counterfactual explanations?","Question",{"text":75,"@type":76},"PACE targets the gap between prediction-changing counterfactuals and recommendations that are realistic or implementable. It does so by enforcing domain knowledge and feasible intervention constraints during generation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the PACE framework combine neural and symbolic components?",{"text":80,"@type":76},"PACE uses a neural predictive model for classification and a symbolic reasoning layer to enforce domain-specific constraints. The symbolic layer constrains which interventions are admissible when generating counterfactuals.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the experimental setup for evaluating PACE?",{"text":84,"@type":76},"The case study uses the Adult Income dataset, combining an MLP classifier with Answer Set Programming (ASP) rules. 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