Explainable and Interpretable AI

Dear Colleagues,

Over the last few years, the European Union and its Member States have released several documents to define an ethical framework for Artificial Intelligence (AI) and a draft for a future regulation (the AI Act). Guidelines such as the Trustworthy AI Guidelines from the High-Level Expert Group and national strategies by EU Member States promote the inclusion of ethical principles such as fairness, accountability, or transparency. Accordingly, the AI community has been moving toward the operationalization of responsible practices of AI design, development, and use. In particular, the extended use of deep neural networks within applications that are now classified as high-risk by the AI Act, such as the use of facial recognition in law enforcement or healthcare, has raised several ethical and legal concerns regarding their design, but also the social and environmental impact. Indeed, these AI-based systems are also known as black-box or opaque models due to their lack of interpretability.

Explainable and interpretable AI (XAI/IAI) is an active area of research that aims to contribute to build a culture of best practices to achieve responsible and trustworthy AI. It focuses on developing methods to better understand the process behind the algorithms, while establishing levels of explainability adapted to different user-oriented audiences, enhancing the decision-making power of AI stakeholders by providing tools for assessment, understandability, and interaction with AI-based systems.

This Special Issue aims to collect high-quality, original state-of-the-art papers that present novel research including the following non-exhaustive list of topics:

• Bias detection/evaluation/removal
• Ethical and legal aspects of XAI/IAI
• Evaluation metrics
• Human-understandable explanations
• Epistemic aspects of XAI
• White-box approaches
• Applications of XAI/IAI in different domains

Dr. Atia Cortés
Dr. Francisco Grimaldo
Dr. Daniel Garcia-Costa
Guest Editors

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