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Found 14 papers, 4 papers with code
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.
Argumentative Large Language Models for Explainable and Contestable Decision-Making
Concretely, we introduce argumentative LLMs, a method utilising LLMs to construct argumentation frameworks, which then serve as the basis for formal reasoning in decision-making.
Interval Abstractions for Robust Counterfactual Explanations
Counterfactual Explanations (CEs) have emerged as a major paradigm in explainable AI research, providing recourse recommendations for users affected by the decisions of machine learning models.
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.
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.
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.
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.
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.
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.
Forecasting Argumentation Frameworks
We introduce Forecasting Argumentation Frameworks (FAFs), a novel argumentation-based methodology for forecasting informed by recent judgmental forecasting research.
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.
Argumentative XAI: A Survey
Explainable AI (XAI) has been investigated for decades and, together with AI itself, has witnessed unprecedented growth in recent years.
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
