Wassila Ouerdane

EUMAS Conference 2025 Conference Paper

Explaining Why Fair Roommate Matchings Do Not Exist

• Wassila Ouerdane
• Francesco Sabatino
• Anaëlle Wilczynski

ECAI Conference 2025 Conference Paper

FactNET: A Granular Fact-Checking Assistant Framework for Complex Claims

• Géraud Faye
• Wassila Ouerdane
• Guillaume Gadek
• Sylvain Gatepaille
• Céline Hudelot

This paper presents FactNET, a framework for human-centered granular fact-checking. Each claim to check is decomposed into sub claims that are fact-checked independently by using an LLM’s internal knowledge. The user interacts with the framework through an innovative interface, displaying the complex claim in the form of a graph, allowing the input of human knowledge on the topic, and evaluating the trust given in the generated evidence. A video demonstrating the system is available at TO_BE_PUBLISHED (in submission documents for review phase).

ECAI Conference 2024 Conference Paper

Interpretable Image Classification Through an Argumentative Dialog Between Encoders

• Dao Thauvin
• Stéphane Herbin
• Wassila Ouerdane
• Céline Hudelot

We address the problem of designing interpretable algorithms for image classification. Modern computer vision algorithms implement classification in two phases: feature extraction - the encoding - that relies on deep neural networks (DNN), followed by a task-oriented decision - the decoding - often also using a DNN. We propose to formulate this last phase as an argumentative DialoguE Between two agents relying on visual ATtributEs and Similarity to prototypes (DEBATES). DEBATES represents the combination of information provided by two encoders in a transparent and interpretable way. It relies on a dual process that combines similarity to prototypes and visual attributes, each extracted from an encoder. DEBATES makes explicit the agreements and conflicts between the two encoders managed by the two agents, reveals the causes of unintended behaviors, and helps identify potential corrective actions to improve performance. The approach is demonstrated on two problems of fine-grained image classification.

IJCAI Conference 2019 Conference Paper

Comparing Options with Argument Schemes Powered by Cancellation

• Khaled Belahcene
• Christophe Labreuche
• Nicolas Maudet
• Vincent Mousseau
• Wassila Ouerdane

We introduce a way of reasoning about preferences represented as pairwise comparative statements, based on a very simple yet appealing principle: cancelling out common values across statements. We formalize and streamline this procedure with argument schemes. As a result, any conclusion drawn by means of this approach comes along with a justification. It turns out that the statements which can be inferred through this process form a proper preference relation. More precisely, it corresponds to a necessary preference relation under the assumption of additive utilities. We show the inference task can be performed in polynomial time in this setting, but that finding a minimal length explanation is NP-complete.

IJCAI Conference 2018 Conference Paper

Accountable Approval Sorting

• Khaled Belahcene
• Yann Chevaleyre
• Christophe Labreuche
• Nicolas Maudet
• Vincent Mousseau
• Wassila Ouerdane

We consider decision situations in which a set of points of view (voters, criteria) are to sort a set of candidates to ordered categories (Good/Bad). Candidates are judged good, when approved by a sufficient set of points of view; this corresponds to NonCompensatory Sorting. To be accountable, such approval sorting should provide guarantees about the decision process and decisions concerning specific candidates. We formalize accountability using a feasibility problem expressed as a boolean satisfiability formulation. We illustrate different forms of accountability when a committee decides with approval sorting and study the information that should be disclosed by the committee.

IJCAI Conference 2017 Conference Paper

A Model for Accountable Ordinal Sorting

• Khaled Belahcene
• Christophe Labreuche
• Nicolas Maudet
• Vincent Mousseau
• Wassila Ouerdane

We address the problem of multicriteria ordinalsorting through the lens of accountability, i. e. theability of a human decision-maker to own a recommendationmade by the system. We put forward anumber of model features that would favor the capabilityto support the recommendation with a convincingexplanation. To account for that, we designa recommender system implementing and formalizingsuch features. This system outputs explanationsdefined under the form of specific argumentschemes tailored to represent the specific rules ofthe model. At the end, we discuss possible andpromising argumentative perspectives.

ECAI Conference 2012 Conference Paper

Justifying Dominating Options when Preferential Information is Incomplete

• Christophe Labreuche
• Nicolas Maudet
• Wassila Ouerdane

Providing convincing explanations to accompany recommendations is a key issue in decision-aiding. In the context of decisions involving multiple criteria, the problem is made very difficult because the decision model itself may involve a complex process. In this paper, we investigate the following issue: when the preferential information provided by the user is incomplete, is there a principled way to define what is a "simple" explanation for a recommended choice? We argue first that explanations may necessitate different levels of detail. Next, we show that even when a detailed explanation is necessary, it is possible to distinguish explanations of different levels of complexity. Our results rely on an original connection we establish between the "mechanics" required to compute supporting coalitions of criteria and the simplicity of the explanation.

ECAI Conference 2010 Conference Paper

Dealing with the dynamics of proof-standard in argumentation-based decision aiding

• Wassila Ouerdane
• Nicolas Maudet
• Alexis Tsoukiàs

Usually, in argumentation, the proof-standards that are used are fixed a priori by the procedure. However (multicriteria) decision-aiding is a context where it may be modified dynamically during the process, depending on the responses of the decision-maker. The expert indeed needs to adapt and refine its choice of an appropriate method of aggregating arguments pros and cons, so that it fits the preference model inferred from the interaction. In this short paper we introduce how this aspect can be handled in an argumentation-based decision-aiding framework. The first contribution of the paper is conceptual: the notion of a concept lattice based on simple properties and allowing to navigate among the different proof-standards is put forward. We then show how this can be integrated within the Carneades model.