Featured Paper

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.

Bridging the Gap Between AI and Explainability in the GDPR: Towards Trustworthiness-by-Design in Automated Decision-Making
Ronan Hamon, Henrik Junklewitz, Ignacio Sanchez, Gianclaudio Malgieri, and Paul De Hert
IEEE Computational Intelligence Magazine (Volume: 17, Issue: 1, Feb. 2022)

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