From the perspective of machine learning (ML), we live in happy times. For many tasks we know not one, but many different ML algorithms or models we can select from and achieve at least a decent performance. This wealth of models and their variations introduces a challenge – we need to find such configuration that fits our task and data.
To find the right model, we need to define the criteria that measure how well a particular model and its parameters and hyperparameters fit the problem at hand. Then, we usually do some kind of hyperparameter optimization or Automated Machine Learning (AutoML) [1].
In recent years, the number of post-hoc XAI methods became similarly overwhelming like the number of different machine learning methods. To find a post-hoc explainability algorithm that provides good explanations for the task at hand, we can borrow the concepts from AutoML. Like in AutoML, we have a space of available algorithms and their configurations, and we want to find the one that provides good explanations. The challenging part of AutoXAI is how to compare different explainability algorithms. In other words – what’s a good explanation?
According to multiple authors, a good explanation should balance between two properties – it should faithfully describe a model’s behavior and be understandable for humans.
We proposed a definition of AutoXAI as an optimization problem. Through optimization, we want to find an explainability algorithm that maximizes two sets of criteria – understandability and fidelity. These criteria measure the quality of explanations with respect to the underlying model and data.
The first set of criteria, understandability, measures how similar the explanations generated by the explainability algorithm for predictions made by the model are to the explanations that the user considers understandable.
The second set of criteria, fidelity, ensure that the explanations truly reflect the decision-making process of the model.
We conducted three experiments on three different classification tasks. In two tasks, we classified images from magnetic resonance as either healthy or not. In the last task, we classified sentiment of short textual reviews. For these we wanted to find a configuration of a particular explainability algorithm – Layerwise relevance propagation. We proposed three understandability measures that were maximized by using a modified Particle Swarm Optimization in order to obtain understandable explanations.
The results of the proposed method and of the project were presented at the Workshop on Explainable Artificial Intelligence at the International joint conference on artificial intelligence (IJCAI) 2022 in Vienna and at a public seminar organized by KInIT. Proceedings from the conference workshop can be found here.
According to the number of papers related to Explainable AI published in recent years, it is clear that this topic drew attention in the scientific community. However, popularization and promotion of Explainable AI in the industry and general public is equally important.
Based on knowledge acquired in our own research and study of relevant scientific literature, we prepared a series of five popularization articles. We covered various topics, from general description of Explainable AI to measuring quality of explanations obtained by using different methods.
Selected aspects of Explainable AI were presented in a technical talk at the Better AI Meetup on November 9th, 2022. You can watch the recording here:
Martin Tamajka, Researcher
Kempelen Institute of Intelligent Technologies
The PricewaterhouseCoopers Endowment Fund at the Pontis Foundation supported this project.
[1] Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren. Automated machine learning: methods, systems, challenges. Springer Nature, 2019.
