IEEE Intelligent Systems is the first IEEE publication on artificial intelligence (AI) and has been publishing trusted content for more than 35 years. It bridges the gap between AI theories and practices. It publishes original, significant, and timely leading-edge theories and technologies of AI that perceive, reason, learn, discover, optimize, act, communicate, and reflect humanly, rationally, and ethically. It serves AI scientists, engineers, policymakers, and students of all levels.

IEEE Intelligent Systems is a peer-reviewed bimonthly publication of the IEEE Computer Society. It is sponsored by the Association for the Advancement of Artificial Intelligence (AAAI). Our technical cosponsors are the British Computer Society (BCS) and the European Association for Artificial Intelligence (EurAI).

Scope

IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications. Typically they are degreed professionals, with backgrounds in engineering, hard science, or business. The publication emphasizes current practice and experience, together with promising new ideas that are likely to be used in the near future. Sample topic areas for feature articles include knowledge-based systems, intelligent software agents, natural-language processing, technologies for knowledge management, machine learning, data mining, adaptive and intelligent robotics, knowledge-intensive processing on the Web, and social issues relevant to intelligent systems. Also encouraged are application features, covering practice at one or more companies or laboratories; full-length product stories (which require refereeing by at least three reviewers); tutorials; surveys; and case studies. Often issues are theme-based and collect articles around a contemporary topic under the auspices of a guest editor working with the editor in chief.

Special Issue on Explicable Artificial Intelligence for Affective Computing
Submission Date: 2025-02-01

Background:

As Artificial Intelligence (AI) advances, the need for transparency and interpretability in its decision-making processes becomes more pronounced, especially within the domain of affective computing. The capacity of AI systems to comprehend and react to human emotions introduces ethical considerations, necessitating a delicate equilibrium between innovation and accountability. Various stakeholders, spanning end-users, developers, and policymakers, express a collective need for a more profound comprehension of these systems, particularly in emotionally charged situations.

The motivation of this Special Issue stems from the inherent challenges in creating AI models that not only accurately recognize and respond to human emotions but also provide clear, interpretable insights into their decision-making processes. The Special Issue also aims at enriching the connotation of Explicable AI with diverse and comprehensive dimensions. Expanding the meaning of explicability is not just about deciphering the “black box” nature of AI models; it involves a broader understanding that encapsulates various facets crucial for fostering user trust, ethical considerations, and interdisciplinary collaboration.

Topics:

    Explainable sentiment analysis, emotion detection, and figurative language processing
    Neurosymbolic affective computing
    Multimodal affective computing with explainability
    Affective intention awareness AI
    Trustworthy AI for affective computing
    Affective computing involves multidisciplinary ensemble and explainability
    Affective computing for science research, e.g., healthcare, education, behavioural, cognitive and social science
    Granular task decomposition for affective computing
    Ethical analysis pertains to Explicable AI for affective computing.

Highlights:

The Special Issue will consider papers on the mentioned topics that demonstrate humanitarian value. While achieving state-of-the-art performance is commendable, acceptance priority will be given to works that contribute to the advancement of seven pillars for future AI, including Multidisciplinarity, Task Decomposition, Parallel Analogy, Symbol Grounding, Similarity Measure, Intention Awareness, and Trustworthiness. All submissions to the Special Issue undergo a rigorous editorial pre-screening process to assess their relevance, quality, and originality. This initial screening ensures that the manuscripts align with the thematic focus of the Special Issue and meet the Journal’s standards.

Evaluation Criteria:

The evaluation of submitted papers will be guided by the following key questions: 

    a) Does the paper contribute to explicable AI in the context of affective computing? 
    b) Does the paper provide an adequate level of technical innovation and/or analytical insights?
    c) Are the findings or contributions supported by experimental evidence and/or theoretical underpinning? 
    d) Is the paper appropriate to be published on IEEE Intelligent Systems?

Peer Review:

The papers will be peer-reviewed by at least three independent reviewers with expertise in the area.