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Author Bertossi, L.; Li, J.; Schleich, M.; Suciu, D.; Vagena, Z. doi  isbn
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  Title (up) Causality-based Explanation of Classification Outcomes Type
  Year 2020 Publication DEEM'20: Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning Abbreviated Journal DEEM 2020  
  Volume 6 Issue Pages 1-10  
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  ISSN ISBN 9781450380232 Medium  
  Area Expedition Conference  
  Notes Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1309  
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Author Bertossi, L.; Geerts, F. doi  openurl
  Title (up) Data Quality and Explainable AI Type
  Year 2020 Publication Acm Journal Of Data And Information Quality Abbreviated Journal ACM J. Data Inf. Qual.  
  Volume 12 Issue 2 Pages 11  
  Keywords Machine learning; causes; fairness; bias  
  Abstract In this work, we provide some insights and develop some ideas, with few technical details, about the role of explanations in Data Quality in the context of data-based machine learning models (ML). In this direction, there are, as expected, roles for causality, and explainable artificial intelligence. The latter area not only sheds light on the models, but also on the data that support model construction. There is also room for defining, identifying, and explaining errors in data, in particular, in ML, and also for suggesting repair actions. More generally, explanations can be used as a basis for defining dirty data in the context of ML, and measuring or quantifying them. We think dirtiness as relative to the ML task at hand, e.g., classification.  
  Address [Bertossi, Leopoldo] Univ Adolfo Ibanez, Fac Engn & Sci, Santiago, Chile, Email: leopoldo.bertossi@uai.cl;  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1936-1955 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000582595600005 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1308  
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Author Bertossi, L. doi  openurl
  Title (up) Declarative Approaches to Counterfactual Explanations for Classification Type
  Year 2022 Publication Theory and Practice of Logic Programming Abbreviated Journal Theory Pract. Log. Program.  
  Volume Early Access Issue Pages  
  Keywords classification; explanations; counterfactuals; causality; answer-set programming; constraints  
  Abstract We propose answer-set programs that specify and compute counterfactual interventions on entities that are input on a classification model. In relation to the outcome of the model, the resulting counterfactual entities serve as a basis for the definition and computation of causality-based explanation scores for the feature values in the entity under classification, namely responsibility scores. The approach and the programs can be applied with black-box models, and also with models that can be specified as logic programs, such as rule-based classifiers. The main focus of this study is on the specification and computation of best counterfactual entities, that is, those that lead to maximum responsibility scores. From them one can read off the explanations as maximum responsibility feature values in the original entity. We also extend the programs to bring into the picture semantic or domain knowledge. We show how the approach could be extended by means of probabilistic methods, and how the underlying probability distributions could be modified through the use of constraints. Several examples of programs written in the syntax of the DLV ASP-solver, and run with it, are shown.  
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  ISSN 1471-0684 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000734688600001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1515  
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Author Livshits, E.; Bertossi, L.; Kimelfeld, B.; Sebag, M. openurl 
  Title (up) Query Games in Databases Type
  Year 2021 Publication Sigmod Record Abbreviated Journal Sigmod Rec.  
  Volume 50 Issue 1 Pages 78-85  
  Keywords RESPONSIBILITY; ANSWERS; BLAME  
  Abstract Database tuples can be seen as players in the game of jointly realizing the answer to a query. Some tuples may contribute more than others to the outcome, which can be a binary value in the case of a Boolean query, a number for a numerical aggregate query, and so on. To quantify the contributions of tuples, we use the Shapley value that was introduced in cooperative game theory and has found applications in a plethora of domains. Specifically, the Shapley value of an individual tuple quantifies its contribution to the query. We investigate the applicability of the Shapley value in this setting, as well as the computational aspects of its calculation in terms of complexity, algorithms, and approximation.  
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  ISSN 0163-5808 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000737738900017 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1517  
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Author Bertossi, L. doi  openurl
  Title (up) Specifying and computing causes for query answers in databases via database repairs and repair-programs Type
  Year 2021 Publication Knowledge And Information Systems Abbreviated Journal Knowl. Inf. Syst.  
  Volume 63 Issue Pages 199231  
  Keywords Causality; Databases; Repairs; Constraints; Answer-set programming  
  Abstract There is a recently established correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints. In this work, answer-set programs that specify database repairs are used as a basis for solving computational and reasoning problems around causality in databases, including causal responsibility. Furthermore, causes are introduced also at the attribute level by appealing to an attribute-based repair semantics that uses null values. Corresponding repair-programs are introduced, and used as a basis for computation and reasoning about attribute-level causes. The answer-set programs are extended in order to capture causality under integrity constraints.  
  Address [Bertossi, Leopoldo] Adolfo Ibanez Univ, Fac Engn & Sci, Santiago, Chile, Email: leopoldo.bertossi@uai.cl  
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  Publisher Springer London Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0219-1377 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000584967200001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1258  
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Author Livshits, E.; Bertossi, L.; Kimefeld, B.; Sebag, M. doi  openurl
  Title (up) The Shapley Value of Tuples in Query Answering Type
  Year 2021 Publication Logical Methods in Computer Science Abbreviated Journal Log. Methods Comput. Sci.  
  Volume 17 Issue 3 Pages 22  
  Keywords Shapley value; query answering; conjunctive queries; aggregate queries  
  Abstract We investigate the application of the Shapley value to quantifying the contribution of a tuple to a query answer. The Shapley value is a widely known numerical measure in cooperative game theory and in many applications of game theory for assessing the contribution of a player to a coalition game. It has been established already in the 1950s, and is theoretically justified by being the very single wealth-distribution measure that satisfies some natural axioms. While this value has been investigated in several areas, it received little attention in data management. We study this measure in the context of conjunctive and aggregate queries by defining corresponding coalition games. We provide algorithmic and complexity-theoretic results on the computation of Shapley-based contributions to query answers; and for the hard cases we present approximation algorithms.  
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  ISSN 1868-8969 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000701675700007 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1310  
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