Featured articles in IEEE CIS Transactions/Magazine are selected by the Editor-in-Chiefs on the basis of a number of factors including the perceived likely scientific interest in the paper, its novelty and contribution, its timeliness etc., in order to showcase a diverse and balanced set of topics from their individual Transactions/Magazine. Due to the promotion context of featured articles, the selection is left at the discretion of the Editor-in-Chief to identify articles that he/she sees relevant to the promotion. The decision of the Editor-in-Chief is final.

Abstract: Can satisfactory explanations for complex machine learning models be achieved in high-risk automated decision-making? How can such explanations be integrated into a data protection framework safeguarding a right to explanation? This article explores from an interdisciplinary point of view the connection between existing legal requirements for the explainability of AI systems set out in the General Data Protection Regulation (GDPR) and the current state of the art in the field of explainable AI. It studies the challenges of providing human legible explanations for current and future AI-based decision-making systems in practice, based on two scenarios of automated decision-making in credit scoring risks and medical diagnosis of COVID-19. These scenarios exemplify the trend towards increasingly complex machine learning algorithms in automated decision-making, both in terms of data and models. Current machine learning techniques, in particular those based on deep learning, are unable to make clear causal links between input data and final decisions. This represents a limitation for providing exact, human-legible reasons behind specific decisions, and presents a serious challenge to the provision of satisfactory, fair and transparent explanations. Therefore, the conclusion is that the quality of explanations might not be considered as an adequate safeguard for automated decision-making processes under the GDPR. Accordingly, additional tools should be considered to complement explanations. These could include algorithmic impact assessments, other forms of algorithmic justifications based on broader AI principles, and new technical developments in trustworthy AI. This suggests that eventually all of these approaches would need to be considered as a whole.

Index Terms: Law, Decision making, Data models, General Data Protection Regulation, Machine learning algorithms, Deep learning, Security, Decision making, Data models, COVID-19

IEEE Xplore Link: https://ieeexplore.ieee.org/document/9679770

Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary Algorithms [Research Frontier]
Hisao Ishibuchi, Lie Meng Pang, and Ke Shang
IEEE Computational Intelligence Magazine (Volume: 17, Issue: 1, Feb. 2022)

Abstract: The performance of a newly designed evolutionary algorithm is usually evaluated by computational experiments in comparison with existing algorithms. However, comparison results depend on experimental setting; thus, fair comparison is difficult. Fair comparison of multi-objective evolutionary algorithms is even more difficult since solution sets instead of solutions are evaluated. In this paper, the following four issues are discussed for fair comparison of multi-objective evolutionary algorithms: (i) termination condition, (ii) population size, (iii) performance indicators, and (iv) test problems. Whereas many other issues related to computational experiments such as the choice of a crossover operator and the specification of its probability can be discussed for each algorithm separately, all the above four issues should be addressed for all algorithms simultaneously. For each issue, its strong effects on comparison results are first clearly demonstrated. Then, the handling of each issue for fair comparison is discussed. Finally, future research topics related to each issue are suggested.

Index Terms: Social factors, Evolutionary computation, Robustness, Statistics, Optimization, Convergence

IEEE Xplore Link: https://ieeexplore.ieee.org/document/9679762

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Information for Special Issues

IEEE Computational Intelligence Magazine (IEEE CIM) publishes special issues on emerging topics guest edited by distinguished researchers in computational intelligence. Here is some information about how a special issue is organized.

Phase 1 – Proposal: Interested researchers should submit a proposal for the special issue on an emerging topic in computational intelligence, its application fields, or its closely related areas. The proposal should be sent to the Editor-in-Chief (EiC) of IEEE CIM. The proposal should include at least the following components: the theme of the special issue, its relevance/importance and need in the present context, a list of specific topics, a list of potential authors, a feasible time table, and CVs of all proposed guest editors. Note that, the list of potential authors is needed just to get an idea that there are enough researchers to support such a special issue. The special issue would be organized based on an open call for paper and we do not consider special issues based on papers from a conference.

Guest editors must be senior researchers (at least 5 years beyond terminal degree) with significant publication records directly related to the special issue topic area. Past editorial board experience is not required but highly desirable. No more than 4 guest editors will be approved. One guest editor must be a current associate editor of the IEEE Computational Intelligence Magazine.

Phase 2 – Evaluation: The EiC gets the proposal evaluated by several associate editors of IEEE CIM. They take into account various issues including the technical merit, need and relevance, timeliness, and feasibility of such a special issue. Based on the input from the associate editors, the EiC makes a decision on the special issue proposal. The decision could be acceptance, rejection or a revision of the proposal. A revised proposal may again be reviewed by the some associate editors.

Phase 3 – Call for Papers: If a proposal is accepted, the guest editors are asked to prepare a call for papers (CFP) formatted to one IEEE CIM page so that it can be published in IEEE CIM. The CFP should include all relevant information such as the theme, topics, deadlines, and submission guidelines. The CFP is usually announced through IEEE CIM website, CIS Newsletter and published in the CIS Transactions and Magazine.

Phase 4 – Processing: The review process of submitted special issue papers is handled by the guest editors. The review process for special issue papers is exactly the same as that for regular papers. If a paper is authored by any guest editor, then reviewing of that manuscript will be handled by a different associate editor chosen by the EiC. The typical number of accepted special issue papers is three or four. Due to a severe total page number limitation of IEEE CIM, it is not likely that more than four special issue papers can be published in a single issue of IEEE CIM.

Phase 5 – Publication: Once the special issue review process is complete, the EiC requests the guest editors to write a preface to the special issue (usually not more than 2 formatted transactions pages) for inclusion in the special issue. The special issue is then published as soon as possible. Some general points: We do not encourage guest editors to submit papers to the special issue and under no circumstances the guest editors should submit more than one manuscript to the special issue. Please contact the EiC for further information.

Call for Special Issues

• Nov 2017: Computational Intelligence in Aerospace Science and Engineering
• Feb 2018: Computational Intelligence for Mobile Network Optimization
• May 2018: Automated Design of Machine Learning and Search Algorithms
• Aug 2018: Computational Intelligence Techniques in Bioinformatics and Bioengineering
• Nov 2018: Computational Intelligence in Finance and Economics
• Feb 2019: Memorial Special Issue for Prof. Lotfi A. Zaheh
• May 2019: Affective Computing and Sentiment Analysis
• Aug 2019: Special Issue on Deep Reinforcement Learning and Games
• Nov 2019: Computational Intelligence for Internet of Things in the Big Data Era
• May 2020: Evolutionary Machine Learning

• Jun 2020: Computational Intelligence for Combating COVID-19
  Aug 2020: Evolutionary Neural Architecture Search and Applications
  Nov 2020: Computational Intelligence for Smart City Services
  Feb 2021: Knowledge Transfer in Evolutionary Optimization
  Feb 2021: Explainable and Trustworthy Artificial Intelligence
  Nov 2021: Computational Intelligence on Meta Learning for Smart Healthcare
  May 2022: Models of Representation in Computational Intelligence
  June 2022: Federated Learning for Future Communication Networks
  Sep 2022: Machine Learning Assisted Evolutionary Multi-objective Optimization

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