Contestable AI needs Computational Argumentation
Not on the map. Why?
Abstract (arXiv)
AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI guidelines (e.g. by the OECD) and regulation of automated decision-making (e.g. GDPR). In this position paper we explore how contestability can be achieved computationally in and for AI. We argue that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes, whereby machines can (i) interact with humans and/or other machines to progressively explain their outputs and/or their reasoning as well as assess grounds for contestation provided by these humans and/or other machines, and (ii) revise their decision-making processes to redress any issues successfully raised during contestation. Given that much of the current AI landscape is tailored to static AIs, the need to accommodate contestability will require a radical rethinking, that, we argue, computational argumentation is ideally suited to support.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 36 of its 128 references that are in the map. First posted on arXiv 17 May 2024.
Builds on
36 of 128 references.
References · 36
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
- 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
- 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,130
- 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
- 2020Artificial Intelligence, Values and Alignmentcited by 443
- 2016Cooperative Inverse Reinforcement Learningcited by 298
- 2017Right for the Right Reasons: Training Differentiable Models by Constraining their Explanationscited by 293
- 2018Abduction-Based Explanations for Machine Learning Modelscited by 159
- 2019Interpretations are useful: penalizing explanations to align neural networks with prior knowledgecited by 144
- 2019Optimal Policies Tend to Seek Powercited by 115
24 more
- 2016The Off-Switch Gamecited by 97
- 2021AI in Finance: Challenges, Techniques and Opportunitiescited by 81
- 2017Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweakingcited by 76
- 2016Research Priorities for Robust and Beneficial Artificial Intelligencecited by 74
- 2021Argumentative XAI: A Surveycited by 66
- 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
- 2021Explanation-Based Human Debugging of NLP Models: A Surveycited by 53
- 2020FIND: Human-in-the-Loop Debugging Deep Text Classifierscited by 40
- 2022Formalising the Robustness of Counterfactual Explanations for Neural Networkscited by 24
- 2023Interactive Explanations by Conflict Resolution via Argumentative Exchangescited by 24
- 2019A Grounded Interaction Protocol for Explainable Artificial Intelligencecited by 22
- 2023From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Spacecited by 20
- 2021Surrogate Model-Based Explainability Methods for Point Cloud NNscited by 18
- 2023SpArX: Sparse Argumentative Explanations for Neural Networks [Technical Report]cited by 18
- 2020Machine Guides, Human Supervises: Interactive Learning with Global Explanationscited by 16
- 2020Three Modern Roles for Logic in AIcited by 15
- 2023Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Disputecited by 9
- 2019Culture-Based Explainable Human-Agent Deconflictioncited by 7
- 2020Contestable Black Boxescited by 6
- 2021Machine Learning for Utility Prediction in Argument-Based Computational Persuasioncited by 6
- 2022Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)cited by 6
- 2014Change in Abstract Argumentation Frameworks: Adding an Argumentcited by 4
- 2021Agree to Disagree: Subjective Fairness in Privacy-Restricted Decentralised Conflict Resolutioncited by 3
- 2023Targeted Activation Penalties Help CNNs Ignore Spurious Signalscited by 3
Built on by
Citing papers per year: 2025: 7; 2026: 5.
12 papers.
- 2026Argumentation for Explainable and Globally Contestable Decision Support with LLMscited by 3
- 2025Explainable AI Systems Must Be Contestable: Here's How to Make It Happencited by 2
- 2025Set Contribution Functions for Quantitative Bipolar Argumentation and their Principlescited by 2
- 2025Argumentative Ensembling for Robust Recourse under Model Multiplicitycited by 1
- 2025Think, Act, Learn: A Framework for Autonomous Robotic Agents using Closed-Loop Large Language Modelscited by 1
- 2025Aristotle's Original Idea: For and Against Logic in the era of AIcited by 0
- 2025Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairnesscited by 0
- 2025Multi-User Personalisation in Human-Robot Interaction: Resolving Preference Conflicts Using Gradual Argumentationcited by 0
- 2026ART: Adaptive Reasoning Trees for Explainable Claim Verificationcited by 0
- 2026CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphscited by 0
- 2026Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordinationcited by 0
- 2026Self-Evolving Multimedia Verification through Memory Consolidation of Contestation Experiencescited by 0
- 2026Self-Evolving Multimedia Verification through Memory Consolidation of Contestation Experiences23 Sep 2026
- 2026Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordination5 Aug 2026
- 2026CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphs5 Aug 2026
- 2026Argumentation for Explainable and Globally Contestable Decision Support with LLMs15 Mar 2026
- 2026ART: Adaptive Reasoning Trees for Explainable Claim Verification9 Jan 2026
- 2025Multi-User Personalisation in Human-Robot Interaction: Resolving Preference Conflicts Using Gradual Argumentation5 Nov 2025
- 2025Set Contribution Functions for Quantitative Bipolar Argumentation and their Principles18 Sep 2025
- 2025Think, Act, Learn: A Framework for Autonomous Robotic Agents using Closed-Loop Large Language Models26 Jul 2025
- 2025Argumentative Ensembling for Robust Recourse under Model Multiplicity25 Jun 2025
- 2025Explainable AI Systems Must Be Contestable: Here's How to Make It Happen2 Jun 2025
- 2025Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness3 Apr 2025
- 2025Aristotle's Original Idea: For and Against Logic in the era of AI15 Mar 2025