# Explaining Random Forests using Bipolar Argumentation and Markov Networks (Technical Report)

arXiv: https://arxiv.org/abs/2211.11699 · HTML: https://mlsanity.com/paper/2211.11699/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [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)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [A Symbolic Approach to Explaining Bayesian Network Classifiers](https://mlsanity.com/paper/1805.03364/) (2018)
- [Model Agnostic Supervised Local Explanations](https://mlsanity.com/paper/1807.02910/) (2018)
- [Argumentative XAI: A Survey](https://mlsanity.com/paper/2105.11266/) (2021)
- [On Explaining Random Forests with SAT](https://mlsanity.com/paper/2105.10278/) (2021)
- [Measurable Counterfactual Local Explanations for Any Classifier](https://mlsanity.com/paper/1908.03020/) (2019)

## Built on by (most cited first, 9 of 9)

- [Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)](https://mlsanity.com/paper/2312.15097/) (2023)
- [Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis](https://mlsanity.com/paper/2401.08879/) (2024)
- [Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)](https://mlsanity.com/paper/2404.14304/) (2024)
- [Latent Debate: A Surrogate Framework for Interpreting LLM Thinking](https://mlsanity.com/paper/2512.01909/) (2025)
- [Set Contribution Functions for Quantitative Bipolar Argumentation and their Principles](https://mlsanity.com/paper/2509.14963/) (2025)
- [Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks](https://mlsanity.com/paper/2409.05831/) (2024)
- [Argumentation and Machine Learning](https://mlsanity.com/paper/2410.23724/) (2024)
- [Surrogate Interpretable Graph for Random Decision Forests](https://mlsanity.com/paper/2506.01988/) (2025)
- [Argumentative Ensembling for Robust Recourse under Model Multiplicity](https://mlsanity.com/paper/2506.20260/) (2025)

## Abstract (arXiv)

Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems. However, as the number of trees and their individual size can be large, their decision making process is often incomprehensible. In order to reason about the decision process, we propose representing it as an argumentation problem. We generalize sufficient and necessary argumentative explanations using a Markov network encoding, discuss the relevance of these explanations and establish relationships to families of abductive explanations from the literature. As the complexity of the explanation problems is high, we discuss a probabilistic approximation algorithm and present first experimental results.