Ethics, Diversity and Consciousness in AI E thics in Artificial Intelligence (AI) is discussed everywhere, in governmental circles as well as scientific forums.It is very often associated with the two widely used concepts of diversity and eXplainable Artificial Intelligence (XAI).The latter was promoted by DARPA 1 , and it is expected to propose methods preserving the rights of users to understand how the AI systems work and why decisions are made.Computational Intelligence methods have much to contribute to this effort to ensure that AI systems are ethical.In July 2020, the European Union has published an Assessment List for Trustworthy Artificial Intelligence (ALTAI) 2 covering a wide range of ethical issues.Among the requirements listed, we find transparency, that encompasses the traceability of the data and model used to make a decision, the explainability of decisions and the communication with the user.Transparency mainly corresponds to the general approach of XAI and Computational Intelligence has much to do to participate in the effort of transparency of XAI, by means of the various methods of automatic generation of explanations, the use of counterfactuals, the help of visualization or the construction of fuzzy systems providing interpretability and expressiveness, for example.Another ALTAI requirement sounds familiar to us, dealing with diversity, non-discrimination and avoidance of unfair bias.Such concerns are at the heart of the IEEE Computational Intelligence's actions, which is very committed to their respect in its scientific and associative life.It is therefore natural to also consider these properties as essential for all algorithms and products that are parts of AI systems, not only in the collection of data and choice of attributes leading to a decision, but also in the ability of all categories of users to interact with the AI system regardless of their characteristics.Privacy and data protection is another aspect of the respect of human users.Other requirements focus more on the cognitive components of the construction of AI systems, the effect they can have on human users, the possible governance of human agents in AI systems and the integration of AI systems in the human society.The technical robustness of the AI system, its reliability, its security and its capacity to identify and mitigate risks in a transparent way are not less important.Machine ethics and the notions of responsibility of AI systems or artificial moral agents 3 are also topics of research that deserve a lot of attention.Consciousness in AI has been debated for a long time 4 and it can take a form different from human consciousness 5 .Multidisciplinarity, and in particular the cooperation with cognitive

Recent advances in deep learning have had a methodological and practical impact on brain-computer interface (BCI) research. Among the various deep network architectures, convolutional neural networks (CNNs) have been well suited for spatio-spectral-temporal electroencephalogram (EEG) signal representation learning. Most of the existing CNN-based methods described in the literature extract features at a sequential level of abstraction with repetitive nonlinear operations and involve densely connected layers for classification. However, studies in neurophysiology have revealed that EEG signals carry information in different ranges of frequency components. To better reflect these multi-frequency properties in EEGs, we propose a novel deep multi-scale neural network that discovers feature representations in multiple frequency/time ranges and extracts relationships among electrodes, i.e., spatial representations, for subject intention/condition identification. Furthermore, by completely representing EEG signals with spatio-spectral-temporal information, the proposed method can be utilized for diverse paradigms in both active and passive BCIs, contrary to existing methods that are primarily focused on single-paradigm BCIs. To demonstrate the validity of our proposed method, we conducted experiments on various paradigms of active/passive BCI datasets. Our experimental results demonstrated that the proposed method achieved performance improvements when judged against comparable state-of-the-art methods. Additionally, we analyzed the proposed method using different techniques, such as PSD curves and relevance score inspection to validate the multi-scale EEG signal information capturing ability, activation pattern maps for investigating the learned spatial filters, and t-SNE plotting for visualizing represented features. Finally, we also demonstrated our method's application to real-world problems. Based on our experimental results and analyses, we believe that the proposed multi-scale neural network can be useful for various BCI paradigms, as a starting model or as a backbone network in any new BCI experiments.

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