Special Issue "Interpretability, Accountability and Robustness in Machine Learning"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Dr. Laurent Risser
Website SciProfiles
Guest Editor
CNRS - Toulouse Mathematics Institute - Artificial and Natural Intelligence Toulouse Institute
Interests: Explainable Machine Learning; Fair Machine Learning; Regularization in high-dimensional optimization; Medical image analysis

Special Issue Information

Dear Colleagues, 

Applications based on Machine-Learning algorithms have now become predominant to support decision-making in various fields such as online advertising, credit,  risk assessment or insurance. They are also of high interest for autonomous vehicles or healthcare, among others. These algorithms indeed make it possible to quickly and efficiently take automatic decisions, with unprecedented successes. Their decisions however heavily rely on training data, which are potentially biased. In addition, the decisions rules generally cannot be directly explained to Humans. This is particularly true when using Neural-Networks as well as Forest- or Kernel-based models. The training phase finally relies on high dimensional optimization algorithms which do not generally converge to global minima. As a consequence, a critical question recently arose among the population: Do algorithmic decisions convey any type of discrimination against specific population sub-groups? The same question can be asked in industrial applications, where machine learning algorithms could not be robust in critical situations. This opened a new field of research in Machine Learning dealing with the ‘Interpretability, Accountability and Robustness’ of Machine-Learning algorithms, which are at the heart of this special issue.

We invite you to submit high quality papers to the Special Issue on “Interpretability, Accountability and Robustness in Machine Learning”, with subjects covering the whole range from theory to applications. The following is a (non-exhaustive) list of topics of interests:

  • Interpretable and explainable Machine-Learning
  • Fair Machine-Learning
  • Bias Measurement in complex data
  • Applications

Dr. Laurent Risser
Guest Editor

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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Algorithms 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 1000 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.

Keywords

  • Machine-Learning
  • Interpretability
  • Explainability
  • Fairness
  • Algorithmic Bias

Published Papers

This special issue is now open for submission.
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