Fairness and Explanation for Trustworthy AI

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) are increasingly being used to shape our daily lives by making, or at least influencing, decisions with ethical and legal implications in a variety of application areas (from agriculture to zoology). However, due to biased input data and/or flawed algorithms, unfair AI-informed decision-making systems may result in reinforcing discrimination, such as racial/gender bias in AI-informed decision-making, or even in high risk environments due to incorrect decisions, e.g., in medical diagnoses. Furthermore, due to the black-box nature of deep learning, for example, the use of such algorithms requires verification and plausibility checks by experts, especially in high-risk areas, such as health, not only for safety and ethical reasons, but particularly for mandatory legal reasons. Such requirements need to provide re-traceability, explainability, interpretability, and transparency for such AI systems—which is technically challenging. AI explanations will become indispensable in the future to interpret black-box results and provide users with insights into the system's decision-making process. Meanwhile, fairness and explanations are key components in fostering trust and confidence in AI systems. In this Special Issue, we will feature cutting-edge research where fairness and explanations are presented for making trustworthy decisions in AI systems.

This Special Issue invites submissions that feature original research on designing, presenting, and evaluating approaches for fairness and explanations in AI systems. The approaches aim at improving human trust in AI systems.

Dr. Jianlong Zhou
Prof. Dr. Andreas Holzinger
Prof. Dr. Fang Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.