GAIA - Explainable CBR

Explainable CBR

Case-based Reasoning for the explanation of intelligent systems.

About Explainable CBR

The goal of Explainable Artificial Intelligence (XAI) is "to create a suite of new or modified machine learning techniques that produce explainable models that, when combined with effective explanation techniques, enable end users to understand, appropriately trust, and effectively manage the emerging generation of Artificial Intelligence (AI) systems".

XCBR refers to two lines of work:

The use of Case-based Reasoning (CBR) methods as the explanation tecnique to open the black box of other AI techniques like Neural Networks, SVM, genetic algorithms:
- Use of experience and memory-based techniques to generate explanations. Retrieve and reuse human explanations.
- Use of twin systems where the CBR module duplicate or collaborate with the black box AI model. CBR as a second opinion reasoning system in a different reasoning process.
Research to explain complex CBR reasoning processes:
- Explain similarity measures/ adaptation processes.
- Knowledge intensive Explanations based on external resources.
- Explaining recommender systems.

xCOLIBRI

xCOLIBRI is an evolution of the COLIBRI plaform focused on the application of CBR for the explanation of intelligent Systems.

From the CBR perspective, research in XAI has pointed out the importance of taking advantage of the human knowledge to generate and evaluate explanations. Therefore, we have created a version of COLIBRI focused on XAI that supports the development of Case-Based Explanation systems. It provides several implementations -either in Java, Python or JavaScript- that can be integrated into existing AI systems to enhance their explainability.

Go to xCOLIBRI project

CBRex

Case-based Reasoning for the explanation of intelligent systems.
TIN2017-87330-R 2018-2021

mineco

The main goal of this project is the research in techniques to explain artificial intelligent systems in order to increase the transparency and reliability in these systems.
The main contribution of the project to the XAI is the use of Case-based Reasoning (CBR) methods for the inclusion of explanations to several AI techniques using reasoning-by-example. CBR systems have previous experiences in interactive explanations and in exploiting memory-based techniques to generate these explanations. The memory of previous facts and decisions will be the main technique in this project to explain the reasoning behind some AI systems. More precisely, this project will delve into generic explanation techniques, which would be extensible to different domains, symbolic and subsymbolic AI techniques and personalized explanations.

PERXAI

Personalized Explainable Artificial Intelligence from experiential knowledge .
PID2020-114596RB-C21 2021-2023

mineco

The area of research in Explained Artificial Intelligence (XAI) has experienced a considerable boom in recent years, generating a wide interest in both institutions and companies as well as in the academic field. In the last few years, the need to understand the reasons that lead an AI system to reach a conclusion, make a prediction, a reasoning, a recommendation or a decision has increased and in this way users trust the AI system. In this context, numerous XAI techniques have emerged and are applied in a growing number of practical areas.
The PERXAI project is proposed as an extension of the results of our previous CBRex project where we have investigated the application of Case Based Reasoning (CBR) as a subrogated technique to explain AI algorithms. This project has clearly identified the need to approach AI processes that can be explained from a much broader perspective than the XAI algorithm itself, including in this process the data used by the intelligent system and the way in which this explanation is presented -displayed- to the user, where interactivity plays a very important role. Additionally, for the explanation processes to be effective, the need for personalization to the user to whom the explanation is addressed must be taken into account. This process of generating complete and personalized explanations is inherently very complex to develop. However, from the experience of the CBRex project it can be concluded that the explanation processes follow a series of common patterns that can be abstracted from different use cases and reused between different domains.

Thus, the main objective of this project is to build a catalogue of comprehensive explanation strategies that can be captured from the user experience in different domains and applications and can be abstracted, formalized and reused for other domains, applications and users following a CBR approach complemented by an ontology-based semantic tagging that facilitates their transfer to other contexts.