Search Results for author:
Found 92 papers, 29 papers with code
A Graph-Based Method for Unsupervised Knowledge Discovery from Financial Texts
Our method creatively integrates existing resources to construct automatically a knowledge graph of companies and related entities as well as to carry out unsupervised analysis of the resulting graph to provide quantifiable and explainable insights from the produced knowledge.
HILDIF: Interactive Debugging of NLI Models Using Influence Functions
Biases and artifacts in training data can cause unwelcome behavior in text classifiers (such as shallow pattern matching), leading to lack of generalizability.
Exploring the Potential for Large Language Models to Demonstrate Rational Probabilistic Beliefs
Advances in the general capabilities of large language models (LLMs) have led to their use for information retrieval, and as components in automated decision systems.
Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable Explanations
We propose Pub-Guard-LLM, the first large language model-based system tailored to fraud detection of biomedical scientific articles.
Free Argumentative Exchanges for Explaining Image Classifiers
Deep learning models are powerful image classifiers but their opacity hinders their trustworthiness.
A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs
Gradual semantics (GS) have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-world settings, from judgmental forecasting to explainable AI.
Proceedings of the First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024)
Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems.
PeerArg: Argumentative Peer Review with LLMs
We evaluate the performance of the PeerArg pipeline on three different datasets, in comparison with a novel end-2-end LLM that uses few-shot learning to predict paper acceptance given reviews.
Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks
Explaining the strength of arguments under gradual semantics is receiving increasing attention.
Exploring the Effect of Explanation Content and Format on User Comprehension and Trust
In both studies we found a clear preference in terms of subjective comprehension and trust for occlusion-1 over SHAP explanations in general, when comparing based on content.
Learning Brave Assumption-Based Argumentation Frameworks via ASP
Assumption-based Argumentation (ABA) is advocated as a unifying formalism for various forms of non-monotonic reasoning, including logic programming.
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)
We prove that the model inherently follows these preferences when making predictions and show that previous abstract argumentation for case-based reasoning approaches are insufficient at expressing preferences over constituents of an argument.
CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks (Technical Report)
There is a growing interest in understanding arguments' strength in Quantitative Bipolar Argumentation Frameworks (QBAFs).
Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Urban land use inference is a critically important task that aids in city planning and policy-making.
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention
Learning modular object-centric representations is crucial for systematic generalization.
Argumentative Causal Discovery
Causal discovery amounts to unearthing causal relationships amongst features in data.
Contestable AI needs Computational Argumentation
AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable.
On the Correspondence of Non-flat Assumption-based Argumentation and Logic Programming with Negation as Failure in the Head
The relation between (a fragment of) assumption-based argumentation (ABA) and logic programs (LPs) under stable model semantics is well-studied.
Argumentative Large Language Models for Explainable and Contestable Claim Verification
The interpretable nature of these argumentation frameworks and formal reasoning means that any decision made by ArgLLMs may be explained and contested.
Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)
In this paper, we propose a novel theory of Relation Attribution Explanations (RAEs), adapting Shapley values from game theory to offer fine-grained insights into the role of attacks and supports in quantitative bipolar argumentation towards obtaining the arguments' strength.
Interval Abstractions for Robust Counterfactual Explanations
We present procedures to verify $\Delta$-robustness based on Mixed Integer Linear Programming, using which we further propose algorithms to generate CEs that are $\Delta$-robust.
Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation
We make use of a semantics-preserving translation between ABA and bipolar argumentation frameworks (BAFs).
Towards a Framework for Evaluating Explanations in Automated Fact Verification
As deep neural models in NLP become more complex, and as a consequence opaque, the necessity to interpret them becomes greater.
Can Large Language Models perform Relation-based Argument Mining?
Argument mining (AM) is the process of automatically extracting arguments, their components and/or relations amongst arguments and components from text.
The Reasons that Agents Act: Intention and Instrumental Goals
In addition, we show how our definition relates to past concepts, including actual causality, and the notion of instrumental goals, which is a core idea in the literature on safe AI agents.
Robust Counterfactual Explanations in Machine Learning: A Survey
Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models.
Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis
We present a principle-based analysis of contribution functions for quantitative bipolar argumentation graphs that quantify the contribution of one argument to another.
Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)
Model Multiplicity (MM) arises when multiple, equally performing machine learning models can be trained to solve the same prediction task.
Shapley-PC: Constraint-based Causal Structure Learning with a Shapley Inspired Framework
Causal Structure Learning (CSL), also referred to as causal discovery, amounts to extracting causal relations among variables in data.
Honesty Is the Best Policy: Defining and Mitigating AI Deception
There are a number of existing definitions of deception in the literature on game theory and symbolic AI, but there is no overarching theory of deception for learning agents in games.
ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]
We propose ProtoArgNet, a novel interpretable deep neural architecture for image classification in the spirit of prototypical-part-learning as found, e. g., in ProtoPNet.
CAFE: Conflict-Aware Feature-wise Explanations
Feature attribution methods are widely used to explain neural models by determining the influence of individual input features on the models' outputs.
Technical Report on the Learning of Case Relevance in Case-Based Reasoning with Abstract Argumentation
Specifically, we show that, for two legal datasets, AA-CBR and decision-tree-based learning of case relevance perform competitively in comparison with decision trees.
Targeted Activation Penalties Help CNNs Ignore Spurious Signals
Recent methods tackle this problem by training NNs with additional ground-truth annotations of such signals.
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
In this work, we propose Provably RObust and PLAusible Counterfactual Explanations (PROPLACE), a method leveraging on robust optimisation techniques to address the aforementioned limitations in the literature.
Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task
The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal textual entailment.
ABA Learning via ASP
Recently, ABA Learning has been proposed as a form of symbolic machine learning for drawing Assumption-Based Argumentation frameworks from background knowledge and positive and negative examples.
Understanding ProbLog as Probabilistic Argumentation
ProbLog is a popular probabilistic logic programming language/tool, widely used for applications requiring to deal with inherent uncertainties in structured domains.
Proceedings 39th International Conference on Logic Programming
This volume contains the Technical Communications presented at the 39th International Conference on Logic Programming (ICLP 2023), held at Imperial College London, UK from July 9 to July 15, 2023.
Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks (Technical Report)
Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs).
Grounded Object Centric Learning
The extraction of modular object-centric representations for downstream tasks is an emerging area of research.
A negation detection assessment of GPTs: analysis with the xNot360 dataset
Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension.
Dialectical Reconciliation via Structured Argumentative Dialogues
We present a novel framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction.
Learning Assumption-based Argumentation Frameworks
We present a general strategy that applies the transformation rules in a suitable order to learn stratified frameworks, and we also propose a variant that handles the non-stratified case.
Non-flat ABA is an Instance of Bipolar Argumentation
Assumption-based Argumentation (ABA) is a well-known structured argumentation formalism, whereby arguments and attacks between them are drawn from rules, defeasible assumptions and their contraries.
Interactive Explanations by Conflict Resolution via Argumentative Exchanges
As the field of explainable AI (XAI) is maturing, calls for interactive explanations for (the outputs of) AI models are growing, but the state-of-the-art predominantly focuses on static explanations.
SpArX: Sparse Argumentative Explanations for Neural Networks [Technical Report]
Neural networks (NNs) have various applications in AI, but explaining their decisions remains challenging.
Explaining Random Forests using Bipolar Argumentation and Markov Networks (Technical Report)
Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems.
Explaining Image Classification with Visual Debates
An effective way to obtain different perspectives on any given topic is by conducting a debate, where participants argue for and against the topic.
Argumentative Reward Learning: Reasoning About Human Preferences
We define a novel neuro-symbolic framework, argumentative reward learning, which combines preference-based argumentation with existing approaches to reinforcement learning from human feedback.
Formalising the Robustness of Counterfactual Explanations for Neural Networks
Existing attempts towards solving this problem are heuristic, and the robustness to model changes of the resulting CFXs is evaluated with only a small number of retrained models, failing to provide exhaustive guarantees.
On Interactive Explanations as Non-Monotonic Reasoning
To better analyse this issue, in this work we treat explanations as objects that can be subject to reasoning and present a formal model of the interactive scenario between user and system, via sequences of inputs, outputs, and explanations.
A Federated Cox Model with Non-Proportional Hazards
Recent research has shown the potential for neural networks to improve upon classical survival models such as the Cox model, which is widely used in clinical practice.
Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models
We achieve this by using the natural properties of \emph{hyperbolic geometry} to more efficiently model a hierarchy of symbolic features and generate \emph{hierarchical symbolic rules} as part of our explanations.
GLANCE: Global to Local Architecture-Neutral Concept-based Explanations
Specifically, we provide a generator to visualize the `effect' of interactions among features in latent space and draw feature importance therefrom as local explanations.
Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement
Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI.
Forecasting Argumentation Frameworks
We introduce Forecasting Argumentation Frameworks (FAFs), a novel argumentation-based methodology for forecasting informed by recent judgmental forecasting research.
Argumentative Explanations for Pattern-Based Text Classifiers
Hence, we propose AXPLR, a novel explanation method using (forms of) computational argumentation to generate explanations (for outputs computed by PLR) which unearth model agreements and disagreements among the features.
Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)
Neural networks have proven to be effective at solving machine learning tasks but it is unclear whether they learn any relevant causal relationships, while their black-box nature makes it difficult for modellers to understand and debug them.
Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data
There is broad agreement in the literature that explanation methods should be faithful to the model that they explain, but faithfulness remains a rather vague term.
Logically Consistent Adversarial Attacks for Soft Theorem Provers
We propose a novel, generative adversarial framework for probing and improving these models' reasoning capabilities.
Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit
The forum r/AmITheAsshole in Reddit hosts discussion on moral issues based on concrete narratives presented by users.
Explainable Decision Making with Lean and Argumentative Explanations
It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency.
Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification
This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset.
Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation (with Appendix)
We then define a variation of $AA{\text -} CBR_{\succeq}$ which is cautiously monotonic.
An Explanatory Query-Based Framework for Exploring Academic Expertise
The success of research institutions heavily relies upon identifying the right researchers "for the job": researchers may need to identify appropriate collaborators, often from across disciplines; students may need to identify suitable supervisors for projects of their interest; administrators may need to match funding opportunities with relevant researchers, and so on.
Argumentative XAI: A Survey
Explainable AI (XAI) has been investigated for decades and, together with AI itself, has witnessed unprecedented growth in recent years.
Automatic Product Ontology Extraction from Textual Reviews
Ontologies have proven beneficial in different settings that make use of textual reviews.
Explanation-Based Human Debugging of NLP Models: A Survey
Debugging a machine learning model is hard since the bug usually involves the training data and the learning process.
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Data exploration is an important step of every data science and machine learning project, including those involving textual data.
Aggregating Bipolar Opinions (With Appendix)
We introduce a novel method to aggregate Bipolar Argumentation (BA) Frameworks expressing opinions by different parties in debates.
Influence-Driven Explanations for Bayesian Network Classifiers
One of the most pressing issues in AI in recent years has been the need to address the lack of explainability of many of its models.
Deep Argumentative Explanations
Despite the recent, widespread focus on eXplainable AI (XAI), explanations computed by XAI methods tend to provide little insight into the functioning of Neural Networks (NNs).
Explainable Artificial Intelligence (XAI)
Text Classification
Explainable Automated Fact-Checking: A Survey
A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked.
Explainable Automated Fact-Checking for Public Health Claims
We present the first study of explainable fact-checking for claims which require specific expertise.
FIND: Human-in-the-Loop Debugging Deep Text Classifiers
Since obtaining a perfect training dataset (i. e., a dataset which is considerably large, unbiased, and well-representative of unseen cases) is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets.
Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation
Recently, abstract argumentation-based models of case-based reasoning ($AA{\text -}CBR$ in short) have been proposed, originally inspired by the legal domain, but also applicable as classifiers in different scenarios, including image classification, sentiment analysis of text, and in predicting the passage of bills in the UK Parliament.
A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining
Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i. e., support and attack) from text.
Formal Verification of Debates in Argumentation Theory
Such transition systems can model debates and represent their evolution over time using a finite set of states.
Extracting information from free text through unsupervised graph-based clustering: an application to patient incident records
The large volume of text in electronic healthcare records often remains underused due to a lack of methodologies to extract interpretable content.
Human-grounded Evaluations of Explanation Methods for Text Classification
Due to the black-box nature of deep learning models, methods for explaining the models' results are crucial to gain trust from humans and support collaboration between AIs and humans.
Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection
One very important stage in employing stance detection for fake news detection is the aggregation of multiple stance labels from different text sources in order to compute a prediction for the veracity of a claim.
Complexity Results and Algorithms for Bipolar Argumentation
Bipolar Argumentation Frameworks (BAFs) admit several interpretations of the support relation and diverging definitions of semantics.
Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets
In this article, we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others, thus exploiting contextual information.
Argumentation for Explainable Scheduling (Full Paper with Proofs)
Specifically, we define argumentative and natural language explanations for why a schedule is (not) feasible, (not) efficient or (not) satisfying fixed user decisions, based on models of the fundamental makespan scheduling problem in terms of abstract argumentation frameworks (AFs).
Identifying attack and support argumentative relations using deep learning
We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate.
ABA+: Assumption-Based Argumentation with Preferences
We present ABA+, a new approach to handling preferences in a well known structured argumentation formalism, Assumption-Based Argumentation (ABA).
Properties of ABA+ for Non-Monotonic Reasoning
We investigate properties of ABA+, a formalism that extends the well studied structured argumentation formalism Assumption-Based Argumentation (ABA) with a preference handling mechanism.
Justifying Answer Sets using Argumentation
An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not.
Computational Logic Foundations of KGP Agents
This paper presents the computational logic foundations of a model of agency called the KGP (Knowledge, Goals and Plan model.
