Advances in Explainable Artificial Intelligence, 2nd Edition

Dear Colleagues,

Machine Learning (ML)-based Artificial Intelligence (AI) algorithms can learn from known examples of various abstract representations and models that, once applied to unknown examples, can perform classification, regression, or forecasting tasks, to name a few.

Very often, these highly effective ML representations are difficult to understand; this holds true particularly for deep learning models, which can involve millions of parameters. However, for many applications, it is of utmost importance for stakeholders to understand the decisions made by the system, in order to use them better. Furthermore, for decisions that affect an individual, future legislation might even advocate for a “right to an explanation”. Overall, improving the algorithms’ explainability may foster trust and social acceptance of AI.

The need to make ML algorithms more transparent and more explainable has generated several lines of research that form an area known as explainable Artificial Intelligence (XAI).

Among the goals of XAI are adding transparency to ML models by providing detailed information about why the system has reached a particular decision; designing more explainable and transparent ML models, while at the same time maintaining high performance levels; finding a way to evaluate the overall explainability and transparency of the models; and quantifying their effectiveness for different stakeholders.

The objective of this Special Issue is to explore recent advances and techniques in the XAI area.

Research topics of interest include (but are not limited to):

- Devising machine learning models that are transparent by design;

- Planning for transparency, from data collection to training, testing, and production;

- Developing algorithms and user interfaces for explainability;

- Identifying and mitigating biases in data collection;

- Performing black-box model auditing and explanation;

- Detecting data bias and algorithmic bias;

- Learning causal relationships;

- Integrating social and ethical aspects of explainability;

- Integrating explainability into existing AI systems;

- Designing new explanation modalities;

- Exploring theoretical aspects of explanation and interpretability;

- Investigating the use of XAI in application sectors such as healthcare, bioinformatics, multimedia, linguistics, human–computer interaction, machine translation, autonomous vehicles, risk assessment, and justice.

Prof. Dr. Gabriele Gianini
Dr. Pierre-Edouard Portier
Guest Editors

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• machine learning
• deep learning
• explainability
• transparency
• accountability

• Advances in Explainable Artificial Intelligence in Information (11 articles)