Oana Cocarascu

AIJ Journal 2025 Journal Article

Argumentative review aggregation and dialogical explanations

• Antonio Rago
• Oana Cocarascu
• Joel Oksanen
• Francesca Toni

The aggregation of online reviews is one of the dominant methods of quality control for users in various domains, from retail to entertainment. Consequently, explainable aggregation of reviews is increasingly sought-after. We introduce quantitative argumentation technology to this setting, towards automatically generating reasoned review aggregations equipped with dialogical explanations. To this end, we define a novel form of argumentative dialogical agent (ADA), using ontologies to harbour information from reviews into argumentation frameworks. These agents may then be evaluated with a quantitative argumentation semantics and used to mediate the generation of dialogical explanations for item recommendations based on the reviews. We show how to deploy ADAs in three different contexts in which argumentation frameworks are mined from text, guided by ontologies. First, for hotel recommendations, we use a human-authored ontology and exemplify the potential range of dialogical explanations afforded by ADAs. Second, for movie recommendations, we empirically evaluate an ADA based on a bespoke ontology (extracted semi-automatically, by natural language processing), by demonstrating that its quantitative evaluations, which are shown to satisfy desirable theoretical properties, are comparable with those on a well-known movie review aggregation website. Finally, for product recommendation in e-commerce, we use another bespoke ontology (extracted fully automatically, by natural language processing, from a website's reviews) to construct an ADA which is then empirically evaluated favourably against review aggregations from the website.

ECAI Conference 2025 Conference Paper

Individual Consistency eXplorer (ICX): An Interactive Dashboard for the Exploration of Individual Fairness

• Madeleine Waller
• Odinaldo Rodrigues
• Oana Cocarascu

We present ICX (Individual Consistency eXplorer), an interactive dashboard designed to support stakeholders in exploring individual fairness notions within algorithmic decision-making systems. ICX focuses on a set of metrics based on the consistency score, a key measure of individual fairness, by allowing the visualisation of how the classification of an individual compares with that of similar individuals. Stakeholders can define and fine-tune the notion of similarity according to domain-specific criteria, and examine individual-level views that highlight comparable individuals and their classification outcomes. ICX empowers users to interrogate, analyse and interpret fairness at the individual level, making algorithmic decision-making more transparent and accountable.

JAIR Journal 2024 Journal Article

Bias Mitigation Methods: Applicability, Legality, and Recommendations for Development

• Madeleine Waller
• Odinaldo Rodrigues
• Michelle Seng Ah Lee
• Oana Cocarascu

As algorithmic decision-making systems (ADMS) are increasingly deployed across various sectors, the importance of research on fairness in Artificial Intelligence (AI) continues to grow. In this paper we highlight a number of significant practical limitations and regulatory compliance issues associated with the application of existing bias mitigation methods to ADMS. We present an example of an algorithmic system used in recruitment to illustrate these limitations. Our analysis of existing methods indicates a pressing need for a change in the approach to the development of new methods. In order to address the limitations, we provide recommendations for key factors to consider in the development of new bias mitigation methods that aim to be effective in real-world scenarios and comply with legal requirements in the European Union, United Kingdom and United States, such as non-discrimination, data protection and sector-specific regulations. Further, we suggest a checklist relating to these recommendations that should be included with the development of new bias mitigation methods.

ECAI Conference 2024 Conference Paper

EthiX: A Dataset for Argument Scheme Classification in Ethical Debates

• Elfia Bezou-Vrakatseli
• Oana Cocarascu
• Sanjay Modgil

Argument schemes represent stereotypical patterns of reasoning that capture the inferences from premise(s) to conclusion. Despite their usefulness in argument mining, argument scheme classification remains a largely understudied task in NLP. In this paper, we present EthiX, a novel dataset for classifying argument schemes, comprising arguments spanning 22 ethical topics which are manually annotated with argument schemes following Walton’s taxonomy. We evaluate pre-trained models fine-tuned on our dataset and propose a baseline to the community.

AAAI Conference 2024 Conference Paper

Identifying Reasons for Bias: An Argumentation-Based Approach

• Madeleine Waller
• Odinaldo Rodrigues
• Oana Cocarascu

As algorithmic decision-making systems become more prevalent in society, ensuring the fairness of these systems is becoming increasingly important. Whilst there has been substantial research in building fair algorithmic decision-making systems, the majority of these methods require access to the training data, including personal characteristics, and are not transparent regarding which individuals are classified unfairly. In this paper, we propose a novel model-agnostic argumentation-based method to determine why an individual is classified differently in comparison to similar individuals. Our method uses a quantitative argumentation framework to represent attribute-value pairs of an individual and of those similar to them, and uses a well-known semantics to identify the attribute-value pairs in the individual contributing most to their different classification. We evaluate our method on two datasets commonly used in the fairness literature and illustrate its effectiveness in the identification of bias.

AIJ Journal 2021 Journal Article

Argumentative explanations for interactive recommendations

• Antonio Rago
• Oana Cocarascu
• Christos Bechlivanidis
• David Lagnado
• Francesca Toni

A significant challenge for recommender systems (RSs), and in fact for AI systems in general, is the systematic definition of explanations for outputs in such a way that both the explanations and the systems themselves are able to adapt to their human users' needs. In this paper we propose an RS hosting a vast repertoire of explanations, which are customisable to users in their content and format, and thus able to adapt to users' explanatory requirements, while being reasonably effective (proven empirically). Our RS is built on a graphical chassis, allowing the extraction of argumentation scaffolding, from which diverse and varied argumentative explanations for recommendations can be obtained. These recommendations are interactive because they can be questioned by users and they support adaptive feedback mechanisms designed to allow the RS to self-improve (proven theoretically). Finally, we undertake user studies in which we vary the characteristics of the argumentative explanations, showing users' general preferences for more information, but also that their tastes are diverse, thus highlighting the need for our adaptable RS.

NeurIPS Conference 2021 Conference Paper

FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information

• Rami Aly
• Zhijiang Guo
• Michael Schlichtkrull
• James Thorne
• Andreas Vlachos
• Christos Christodoulopoulos
• Oana Cocarascu
• Arpit Mittal

Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation. Existing large-scale benchmarks for this task have focused mostly on textual sources, i. e. unstructured information, and thus ignored the wealth of information available in structured formats, such as tables. In this paper we introduce a novel dataset and benchmark, Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS), which consists of 87, 026 verified claims. Each claim is annotated with evidence in the form of sentences and/or cells from tables in Wikipedia, as well as a label indicating whether this evidence supports, refutes, or does not provide enough information to reach a verdict. Furthermore, we detail our efforts to track and minimize the biases present in the dataset and could be exploited by models, e. g. being able to predict the label without using evidence. Finally, we develop a baseline for verifying claims against text and tables which predicts both the correct evidence and verdict for 18% of the claims.

KR Conference 2020 System Paper

Argumentation as a Framework for Interactive Explanations for Recommendations

• Antonio Rago
• Oana Cocarascu
• Christos Bechlivanidis
• Francesca Toni

As AI systems become ever more intertwined in our personal lives, the way in which they explain themselves to and interact with humans is an increasingly critical research area. The explanation of recommendations is thus a pivotal functionality in a user’s experience of a recommender system (RS), providing the possibility of enhancing many of its desirable features in addition to its effectiveness (accuracy wrt users’ preferences). For an RS that we prove empirically is effective, we show how argumentative abstractions underpinning recommendations can provide the structural scaffolding for (different types of) interactive explanations (IEs), i. e. explanations supporting interactions with users. We prove formally that these IEs empower feedback mechanisms that guarantee that recommendations will improve with time, hence rendering the RS scrutable. Finally, we prove experimentally that the various forms of IE (tabular, textual and conversational) induce trust in the recommendations and provide a high degree of transparency in the RS’s functionality.

ECAI Conference 2020 Conference Paper

Data-Empowered Argumentation for Dialectically Explainable Predictions

• Oana Cocarascu
• Andria Stylianou
• Kristijonas Cyras
• Francesca Toni

Today’s AI landscape is permeated by plentiful data and dominated by powerful data-centric methods with the potential to impact a wide range of human sectors. Yet, in some settings this potential is hindered by these data-centric AI methods being mostly opaque. Considerable efforts are currently being devoted to defining methods for explaining black-box techniques in some settings, while the use of transparent methods is being advocated in others, especially when high-stake decisions are involved, as in healthcare and the practice of law. In this paper we advocate a novel transparent paradigm of Data-Empowered Argumentation (DEAr in short) for dialectically explainable predictions. DEAr relies upon the extraction of argumentation debates from data, so that the dialectical outcomes of these debates amount to predictions (e. g. classifications) that can be explained dialectically. The argumentation debates consist of (data) arguments which may not be linguistic in general but may nonetheless be deemed to be ‘arguments’ in that they are dialectically related, for instance by disagreeing on data labels. We illustrate and experiment with the DEAr paradigm in three settings, making use, respectively, of categorical data, (annotated) images and text. We show empirically that DEAr is competitive with another transparent model, namely decision trees (DTs), while also naturally providing a form of dialectical explanations.

AAMAS Conference 2019 Conference Paper

Extracting Dialogical Explanations for Review Aggregations with Argumentative Dialogical Agents

• Oana Cocarascu
• Antonio Rago
• Francesca Toni

IJCAI Conference 2018 Conference Paper

Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them

• Antonio Rago
• Oana Cocarascu
• Francesca Toni

A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations.