Cognitive computing represents an innovative frontier within computer science, merging artificial intelligence, machine learning, and computational neuroscience to emulate human-like cognitive functions. With rapid developments in natural language processing, pattern recognition, and decision making, cognitive systems have begun to reshape key industries such as healthcare, finance, and education. The field is propelled further by the convergence of big data analytics and the Internet of Things, enabling dynamic, responsive, and highly personalized applications. Despite its potential, the proliferation of cognitive technologies raises pressing concerns related to ethics, trustworthiness, bias, and explainability, prompting vigorous debate and increasing regulatory scrutiny.

Recent research has highlighted significant progress in algorithmic transparency and interpretability, notably driven by advancements in Explainable AI (XAI) and the growing codification of ethical AI standards. Frameworks for responsible AI deployment have emerged, together with methodologies for detecting bias, ensuring accountability, and improving user comprehension of automated decisions. Nonetheless, several challenges endure: inconsistencies in ethical practices, insufficient real-world benchmarking of XAI frameworks, and gaps between technical transparency and layperson understanding. These limitations underscore the necessity for continued exploration of multidisciplinary approaches, the development of robust governance models, and the translation of theory into effective practice.

This Research Topic aims to advance the development of ethical, trustworthy, and explainable cognitive computing systems. Its primary objective is to facilitate the creation, assessment, and implementation of frameworks that embed ethical principles and enhance interpretability in AI-powered technologies. Specific questions to address include: How can cognitive systems be designed to foster user trust? What mechanisms can ensure responsible, fair, and transparent decision-making? What are the technical and societal impacts of deploying these systems at scale? By addressing these questions, this initiative seeks to lay a strong foundation for next-generation cognitive applications that are reliable, transparent, and widely accepted.

This Research Topic welcomes a broad yet focused range of perspectives centering on the ethical and explainable deployment of cognitive computing systems, while emphasizing practical applications and societal impact. Contributions may include theoretical advances, empirical studies, case analyses, and technical reviews. We encourage submissions on, but not limited to, the following themes:

- Innovative approaches and solutions in cognitive computing

- Challenges and ethical dilemmas in AI deployment

- Data-driven decision-making in healthcare

- Explainable AI and cognitive computing in biomedical engineering

- Enhancing efficiency in customer service

- Personalization in education using AI algorithms

- Advanced autonomy in transportation

- Innovations in object recognition and autonomous navigation

- Humanizing robot interactions via natural language processing

- Integrating cognitive technologies in robotics for responsiveness

Manuscript types considered for this Research Topic may include original research articles, review articles, case studies, conceptual analyses, and technical reports.

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Future perspectives on ethical and explainable AI for cognitive computing