Algebraic Aggregation of Random Forests
In my paper, “CHIRPS: Explaining random forest classification”, I took an empirical approach to addressing model transparency by extracting rules that make Random Forest (RF) models more interpretable. Importantly, this was done without sacrificing the high levels of accuracy achieved by the high-performing RF models. The recently published “Algebraic aggregation of random forests: towards explainability and rapid evaluation” by Gossen and Steffen provides a theoretical counterpart, offering essential proofs and a mathematical framework for achieving explainability with RF models.
Explaining Random Forests with Representative Trees
The paper “Can’t see the forest for the trees: Analyzing groves to explain random forests” explores a novel take on model-specific explanations, as outlined in my own research (e.g. you can look at “CHIRPS: Explaining random forest classification” as a reference). This new paper by Szepannek and von Holt seeks to make Random Forests (RF) more interpretable. RF are notoriously hard to explain due to their complexity and these novel methods works well for both classification and regression, which is a very useful extension to the field.
Combatting Fake News With XAI
With all the unhinged hype over ChatGPT stealing everyone’s jobs and AI taking over the world, it’s great to see postiive use cases for Machine Learning (ML) technologies. As usual, eXplainable Artificial Intelligence (XAI) has something to contribute to the ethical landscape of fairness and transparency. In this recent news article we see a concerted attempt to combat fake news with XAI and a pretty sophisticated tech stack. With the rise of social media and other online platforms, the spread of fake news has become a major problem. Fake news is defined as news stories that are intentionally false and designed to mislead readers. It is often spread through social media and can have serious consequences, such as influencing public opinion and even swaying elections.
Explaining Random Forests with Boolean Satisfiability
The paper “On Explaining Random Forests with SAT” uses Boolean satisfiability (SAT) methods to provide a formal framework for generating explanations of Random Forest (RF) predictions. A key result in the paper is that abductive explanations (AXp) and contrastive explanations (CXp) can be derived by encoding the RF’s decision paths into propositional logic. Encoding a decision path as propositional logic, is an entirely reasoned approach and quite straightforward, as I showed in my paper CHIRPS: Explaining random forest classification. The decision paths of an RF model can be transformed into a Boolean formula in Conjunctive Normal Form (CNF). For example, each decision tree in the forest is represented as a set of clauses. Following the paths for a single example prediction essentially carves out a region of the feature space with a set of step functions, resulting in a sub-region that must return the target response. When the clauses of this step functions set correspond to a subset of the features, a change in the remaining feature inputs has no effect on the model prediction. This subset is a prime implicant (PI) explanation.
How Subsets of the Training Data Affect a Prediction
I was quite excited by the title of a new paper, on pre-publication this month. “Explainable Artificial Intelligence: How Subsets of the Training Data Affect a Prediction” by Andreas Brandsæter and Ingrid K. Glad, at first glance, appeared to have some close alignment to my own work CHIRPS: Explaining random forest classification, published earlier this year in June. It’s generally highly desirable to connect with other researchers with which you share common ground, working contemporaneously. Often, fruitful collaborations are born.