# Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking

arXiv: https://arxiv.org/abs/2502.09083 · HTML: https://mlsanity.com/paper/2502.09083/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 67 of its 132 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 30 of 67)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [On the Robustness of Interpretability Methods](https://mlsanity.com/paper/1806.08049/) (2018)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://mlsanity.com/paper/2103.08541/) (2021)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://mlsanity.com/paper/2011.03088/) (2020)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023)
- [Generating Fact Checking Explanations](https://mlsanity.com/paper/2004.05773/) (2020)
- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [What Large Language Models Know and What People Think They Know](https://mlsanity.com/paper/2401.13835/) (2024)
- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019)
- [DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning](https://mlsanity.com/paper/1809.06416/) (2018)

## Built on by (most cited first, 29 of 29)

- [Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoning](https://mlsanity.com/paper/2502.14765/) (2025)
- [TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection](https://mlsanity.com/paper/2509.04448/) (2025)
- [Can Community Notes Replace Professional Fact-Checkers?](https://mlsanity.com/paper/2502.14132/) (2025)
- [Can LLMs Automate Fact-Checking Article Writing?](https://mlsanity.com/paper/2503.17684/) (2025)
- [NeuroGenPoisoning: Neuron-Guided Attacks on Retrieval-Augmented Generation of LLM via Genetic Optimization of External Knowledge](https://mlsanity.com/paper/2510.21144/) (2025)
- [Evaluating open-source Large Language Models for automated fact-checking](https://mlsanity.com/paper/2503.05565/) (2025)
- [Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents](https://mlsanity.com/paper/2507.19090/) (2025)
- [Community Moderation and the New Epistemology of Fact Checking on Social Media](https://mlsanity.com/paper/2505.20067/) (2025)
- [RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking](https://mlsanity.com/paper/2507.09174/) (2025)
- [COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations](https://mlsanity.com/paper/2510.24810/) (2025)
- [Attacks by Content: Automated Fact-checking is an AI Security Issue](https://mlsanity.com/paper/2510.11238/) (2025)
- [Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking](https://mlsanity.com/paper/2603.00267/) (2026)
- [Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine](https://mlsanity.com/paper/2506.20876/) (2025)
- [TSVer: A Benchmark for Fact Verification Against Time-Series Evidence](https://mlsanity.com/paper/2511.01101/) (2025)
- [Explaining Sources of Uncertainty in Automated Fact-Checking](https://mlsanity.com/paper/2505.17855/) (2025)
- [Co-FactChecker: A Framework for Human-AI Collaborative Claim Verification Using Large Reasoning Models](https://mlsanity.com/paper/2604.13706/) (2026)
- [From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence](https://mlsanity.com/paper/2605.06006/) (2026)
- [Adoption of Explainable Natural Language Processing: Perspectives from Industry and Academia on Practices and Challenges](https://mlsanity.com/paper/2508.09786/) (2025)
- [Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning](https://mlsanity.com/paper/2508.10040/) (2025)
- [MultiMind at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment](https://mlsanity.com/paper/2512.20950/) (2025)
- [Multilingual, Multimodal Pipeline for Creating Authentic and Structured Fact-Checked Claim Dataset](https://mlsanity.com/paper/2601.07985/) (2026)
- [MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessment](https://mlsanity.com/paper/2601.22361/) (2026)
- [CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction](https://mlsanity.com/paper/2605.12436/) (2026)
- [Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages](https://mlsanity.com/paper/2605.31136/) (2026)
- [Towards Multi-Agent-Simulation-Based Community Note Evaluation](https://mlsanity.com/paper/2606.18268/) (2026)
- [Evidence Triangulation for Multimodal Fact-Checking in the Wild](https://mlsanity.com/paper/2606.31367/) (2026)
- [MEDIAREF: A Public Knowledge Store for Media Background Checks](https://mlsanity.com/paper/2607.02383/) (2026)
- [SourceMinds at CheckThat! 2026: NLI-Grounded Citation Auditing in a Multi-Agent Pipeline for Full Fact-Checking Article Generation](https://mlsanity.com/paper/2607.24802/) (2026)
- [SoK: Formal Methods for Fact-Checking and Information Integrity](https://mlsanity.com/paper/2609.23239/) (2026)

## Abstract (arXiv)

The pervasiveness of large language models and generative AI in online media has amplified the need for effective automated fact-checking to assist fact-checkers in tackling the increasing volume and sophistication of misinformation. The complex nature of fact-checking demands that automated fact-checking systems provide explanations that enable fact-checkers to scrutinise their outputs. However, it is unclear how these explanations should align with the decision-making and reasoning processes of fact-checkers to be effectively integrated into their workflows. Through semi-structured interviews with fact-checking professionals, we bridge this gap by: (i) providing an account of how fact-checkers assess evidence, make decisions, and explain their processes; (ii) examining how fact-checkers use automated tools in practice; and (iii) identifying fact-checker explanation requirements for automated fact-checking tools. The findings show unmet explanation needs and identify important criteria for replicable fact-checking explanations that trace the model's reasoning path, reference specific evidence, and highlight uncertainty and information gaps.