# Delivering Inflated Explanations arXiv: https://arxiv.org/abs/2306.15272 ยท HTML: https://mlsanity.com/paper/2306.15272/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 68 references that are in the map. - First posted on arXiv 27 Jun 2023. ## Builds on (most cited first, 22 of 22) - [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) - [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017) - [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021) - [Abduction-Based Explanations for Machine Learning Models](https://mlsanity.com/paper/1811.10656/) (2018) - [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017) - [A Symbolic Approach to Explaining Bayesian Network Classifiers](https://mlsanity.com/paper/1805.03364/) (2018) - [Towards Explainable Artificial Intelligence](https://mlsanity.com/paper/1909.12072/) (2019) - [On Explaining Random Forests with SAT](https://mlsanity.com/paper/2105.10278/) (2021) - [On Guaranteed Optimal Robust Explanations for NLP Models](https://mlsanity.com/paper/2105.03640/) (2021) - [On Tackling Explanation Redundancy in Decision Trees](https://mlsanity.com/paper/2205.09971/) (2022) - [Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networks](https://mlsanity.com/paper/2210.13915/) (2022) - [On the Computational Intelligibility of Boolean Classifiers](https://mlsanity.com/paper/2104.06172/) (2021) - [SAT-Based Rigorous Explanations for Decision Lists](https://mlsanity.com/paper/2105.06782/) (2021) - [On Computing Probabilistic Explanations for Decision Trees](https://mlsanity.com/paper/2207.12213/) (2022) - [ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations](https://mlsanity.com/paper/2206.11900/) (2022) - [On Symbolically Encoding the Behavior of Random Forests](https://mlsanity.com/paper/2007.01493/) (2020) - [A unified logical framework for explanations in classifier systems](https://mlsanity.com/paper/2105.14452/) (2021) - [A New Class of Explanations for Classifiers with Non-Binary Features](https://mlsanity.com/paper/2304.14760/) (2023) - [A Symbolic Approach for Counterfactual Explanations](https://mlsanity.com/paper/2206.09638/) (2022) - [On Computing Relevant Features for Explaining NBCs](https://mlsanity.com/paper/2207.04748/) (2022) - [A Logic of "Black Box" Classifier Systems](https://mlsanity.com/paper/2210.07161/) (2022) ## Built on by (most cited first, 16 of 16) - [On Formal Feature Attribution and Its Approximation](https://mlsanity.com/paper/2307.03380/) (2023) - [Language Models as a Service: Overview of a New Paradigm and its Challenges](https://mlsanity.com/paper/2309.16573/) (2023) - [Space Explanations of Neural Network Classification](https://mlsanity.com/paper/2511.22498/) (2025) - [On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems](https://mlsanity.com/paper/2509.25662/) (2025) - [Circuit Representations of Random Forests with Applications to XAI](https://mlsanity.com/paper/2602.08362/) (2026) - [Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimization](https://mlsanity.com/paper/2603.01870/) (2026) - [Uncovering Bugs in Formal Explainers: A Case Study with PyXAI](https://mlsanity.com/paper/2511.03169/) (2025) - [Efficient & Correct Predictive Equivalence for Decision Trees](https://mlsanity.com/paper/2509.17774/) (2025) - [Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms](https://mlsanity.com/paper/2602.13985/) (2026) - [Formal Abductive Explanations for Navigating Mental Health Help-Seeking and Diversity in Tech Workplaces](https://mlsanity.com/paper/2603.14007/) (2026) - [Concisely Explaining the Doubt: Minimum-Size Abductive Explanations for Linear Models with a Reject Option](https://mlsanity.com/paper/2603.14096/) (2026) - [Rigorous Explanations for Tree Ensembles](https://mlsanity.com/paper/2603.29361/) (2026) - [Beyond Explaining Predictions: Logic-Based Explanations for Confidence in Machine Learning Models](https://mlsanity.com/paper/2606.10347/) (2026) - [Are Safety Guarantees in Neural Networks Safe? How to Compute Trustworthy Robustness Certifications](https://mlsanity.com/paper/2606.23858/) (2026) - [Interval Certifications for Multilayered Perceptrons via Lattice Traversal](https://mlsanity.com/paper/2607.08773/) (2026) - [Neuron Activation-based Computation of Logical Explanations for Deep Neural Networks](https://mlsanity.com/paper/2609.14099/) (2026) ## Abstract (arXiv) In the quest for Explainable Artificial Intelligence (XAI) one of the questions that frequently arises given a decision made by an AI system is, ``why was the decision made in this way?'' Formal approaches to explainability build a formal model of the AI system and use this to reason about the properties of the system. Given a set of feature values for an instance to be explained, and a resulting decision, a formal abductive explanation is a set of features, such that if they take the given value will always lead to the same decision. This explanation is useful, it shows that only some features were used in making the final decision. But it is narrow, it only shows that if the selected features take their given values the decision is unchanged. It's possible that some features may change values and still lead to the same decision. In this paper we formally define inflated explanations which is a set of features, and for each feature of set of values (always including the value of the instance being explained), such that the decision will remain unchanged. Inflated explanations are more informative than abductive explanations since e.g they allow us to see if the exact value of a feature is important, or it could be any nearby value. Overall they allow us to better understand the role of each feature in the decision. We show that we can compute inflated explanations for not that much greater cost than abductive explanations, and that we can extend duality results for abductive explanations also to inflated explanations.