This journal is ranked by The Chartered Association of Business Schools' Academic Journal Guide, Australian Business Deans Council, Chinese Academy of Sciences (CAS), China Computer Federation (CCF), BFI (Denmark), Computing Research & Education (CORE) Journal Ranking, The Publication Forum (Finland), Science Citation Index Expanded, Social Sciences Citation Index, Scopus, and SCImago Journal Rank (SJR).

Information Processing and Management publishes cutting-edge original research at the intersection of computing and information science concerning theory, methods, or applications in a range of domains, including but not limited to advertising, business, health, information science, information technology marketing, and social computing.

The journal aims to serve the interests of primary researchers but also practitioners in furthering knowledge at the intersection of computing and information science by providing an effective forum for the timely dissemination of advanced and topical issues. The journal is especially interested in original research articles, research survey articles, research method articles, and articles addressing critical applications of research.

Specifically, the journal is interested in four types of manuscripts, which are:

    Research manuscripts addressing topics at the intersection of computer and information science.
    Methods manuscripts focusing on the application of novel methods at the intersection of computer and information science.
    Review manuscripts assessing, in a critical and in-depth manner, a broad trend at the intersection of computer and information science, providing integration of the prior research, and recommendations for further work in the area.
    Critical application manuscripts concerning system design research at the intersection of computer and information science.

Special Issue on Foundation and Large Language Models
截稿日期: 2025-09-30

Guest editors:

    Yaser Jararweh, Jordan University of Science and Technology, Irbid, Jordan (yaser.amd@gmail.com)
    Sandra Sendra, Polytechnic University of Valencia, Valencia, Spain (sansenco@upv.es)
    Safa Otoum, Zayed University, Dubai, UAE (safa.otoum@zu.ac.ae )
    Yoonhee Kim, Sookmyung Women's University, Korea (yulan@sookmyung.ac.kr)

Special issue information:

Background and Scope:

With the emergence of foundation models (FMs) and Large Language Models (LLMs) that are trained on large amounts of data at scale and adaptable to a wide range of downstream applications, Artificial intelligence is experiencing a paradigm revolution. BERT, T5, ChatGPT, GPT-4, Falcon 180B, Codex, DALL-E, Whisper, and CLIP are now the foundation for new applications ranging from computer vision to protein sequence study and from speech recognition to coding. Earlier models had a reputation of starting from scratch with each new challenge. The capacity to experiment with, examine, and comprehend the capabilities and potentials of next-generation FMs is critical to undertaking this research and guiding its path. Nevertheless, these models are currently inaccessible as the resources required to train these models are highly concentrated in industry, and even the assets (data, code) required to replicate their training are frequently not released due to their demand in the real-time industry. At the moment, mostly large tech companies such as OpenAI, Google, Facebook, and Baidu can afford to construct FMs and LLMS. Despite the expected widely publicized use of FMs and LLMS, we still lack a comprehensive knowledge of how they operate, why they underperform, and what they are even capable of because of their emerging global qualities. To deal with these problems, we believe that much critical research on FMs and LLMS would necessitate extensive multidisciplinary collaboration, given their essentially social and technical structure.

Recommended Topics:

    Architectures and Systems
        Transformers and Attention
        Bidirectional Encoding
        Autoregressive Models
        Prompt Engineering
        Multimodal LLMs
        Fine-tuning
    Challenges
        Hallucination
        Safety and Trustworthiness
        Interpretability
        Fairness
        Social Impact
    Future Directions
        Generative AI
        Explainability and EXplainable AI
        Retrieval Augmented Generation (RAG)
        Federated Learning for FLLM
        Large Language Models Fine-Tuning on Graphs
        Data Augmentation
    Applications
        Natural Language Processing
        Communication Systems
        Security and Privacy
        Image Processing and Computer Vision
        Life Sciences
        Financial Systems

Manuscript submission information:

Important Dates

Manuscript submission due date: September 30th, 2025
Author First notification: October 21st, 2025

Special Issue on Responsible Artificial Intelligence: Methodologies, Implications, and Practices
截稿日期: 2025-10-30

Guest editors:

    Ebrahim Bagheri, University of Toronto, Professor (managing guest editor), ebrahim.bagheri@utoronto.ca
    Robin Cohen, University of Waterloo, Professor, rcohen@uwaterloo.ca
    Faezeh Ensan, Toronto Metropolitan University, Assistant Professor, fensan@torontomu.ca
    Benjamin C. M. Fung, McGill University, Professor, ben.fung@mcgill.ca
    Sébastien Gambs, Université du Québec à Montréal, Professor, gambs.sebastien@uqam.ca
    Reihaneh Rabbany, McGill University, Assistant Professor, reihaneh.rabbany@mcgill.ca

Special issue information:

This special issue seeks to bring together cutting-edge research, methodologies, and critical reflections on Responsible Artificial Intelligence (RAI). The issue aims to deepen our understanding of the ethical, legal, technical, and societal dimensions of AI systems. As AI technologies permeate decision-making across industry, government, and society, the demand for systems that are fair, accountable, transparent, and trustworthy has never been more urgent.

This special issue will provide a dedicated venue for interdisciplinary contributions addressing key challenges and opportunities in designing, deploying, and governing responsible AI systems.

Possible topics of submission:

Submissions are welcome on (but not limited to) the following topics:

    Fairness and Bias Mitigation in AI: Techniques for detecting, measuring, and mitigating bias in data and algorithms.
    Adversarial AI and Red Teaming: Robustness testing, threat modeling, and defense mechanisms;
    Interpretability and Explainability: Models and tools for transparent decision-making.
    Trust, Reliability, and Safety: Trust calibration, assurance testing, and risk management in AI.
    Accountability in Algorithmic Decision-Making: Legal and technical frameworks for recourse and oversight.
    Auditing and Monitoring AI Systems: Processes for real-time evaluation of deployed models.
    Human-Centered AI Design: Participatory design, co-creation, and value-sensitive approaches.
    Sociotechnical and Cultural Dimensions of AI: Historical, social, and cross-cultural studies of AI adoption.
    Environmental Impact of AI: Studies on AI’s carbon footprint and sustainable development.
    Regulatory, Legal, and Policy Considerations: Comparative analyses of AI governance and compliance.
    Responsible AI Education and Training: Curricula design and strategies for teaching AI ethics and safety.
    Social Impact and Labor Implications: Research on justice, equity, and human well-being in AI applications.

Manuscript submission information:

Important dates:

Call for Papers Open: May 2025
Submission Deadline: October 30, 2025

Special Issue on Employing Surveys in the Computational Information Sciences
截稿日期: 2025-12-30

Information Processing & Management invites researchers to submit their original contributions for this special issue on "Employing Surveys in the Computational Information Sciences." This is a ‘standing special issue’, meaning that the special issue is always open for submissions.

Surveys are an integral part of various disciplines and play a crucial role in generating valuable data and insights in a variety of fields. Good survey research has several key characteristics contributing to its quality and effectiveness, including clear research objectives, validity, reliability, a representative sample, a clear survey instrument, ethical considerations, pretesting and piloting, appropriate data analysis, appropriate interpretation, and comprehensive reporting. By incorporating these characteristics into survey research, researchers can enhance their findings' validity, reliability, and impact, ultimately advancing knowledge and understanding in their respective fields.

However, survey data collection by itself is often difficult to publish in the computational information sciences. Computational information science is an interdisciplinary field combining elements of computer science, mathematics, statistics, and information theory to study information representation, processing, and analysis using computational methods. Computational information science focuses on understanding and harnessing the fundamental principles and algorithms that underlie the organization, transmission, storage, and extraction of information in various verticals and domains. This special issue highlights the latest advancements, innovative methodologies, and best practices in survey-based research within computational information science and many related domains.

This Special Issue on Employing Surveys in the Computational Information Sciences seeks to combine cutting-edge research utilizing surveys as a primary data collection method that advances computational information science research. This special Issue promotes interdisciplinary collaboration and presents diverse perspectives on the design, implementation, analysis, and interpretation of survey data within the computational fields. The special issue will encompass a broad scope, including but not limited to social sciences, economics, education, psychology, health, marketing, and public opinion research that uses a survey as the primary data collection method to advance computational information science research.

Note: For this special issue, survey data must be the only or at least the primary mode of data collection.

Possible topics of submissions: The special issue invites original research articles, reviews, case studies, and methodological papers related to research using surveys in computational information sciences. Topics of interest include, but are not limited to:

    User Studies: Surveys to gather user feedback and opinions about computational systems, software interfaces, or algorithms.
    Data Collection for Machine Learning: Surveys to collect labeled or annotated data for training and evaluating machine learning algorithms.
    Data Validation and Verification: Surveys to validate or verify data obtained from other sources.
    Behavioral Studies: Surveys to collect data on user behavior, decision-making processes, and information-seeking strategies.
    Socio-technical Research: Surveys utilized in studying the societal impact, ethics, and implications of computational systems.
    Evaluation and User Experience: Surveys employed to assess the usability, user experience, and satisfaction of computational tools or interfaces.

Guest editors:

Dr. Jim Jansen, 

Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.

Special Issue on Overcoming Publication Bias in Computational Information Science Research
截稿日期: 2025-12-31

Information Processing & Management invites researchers to submit their original contributions for this special issue on Overcoming Publication Bias in Computational Information Science Research. This is a ‘standing special issue’, meaning that the special issue is always open for submissions.

Publication bias refers to the systematic tendency of researchers, authors, and outlets to selectively publish research findings based on their statistical significance, leading to an incomplete and distorted representation of the true body of research evidence. Publication bias occurs when studies with positive or statistically significant results are more likely to be published, while studies with negative or non-significant results are often overlooked or deemed less worthy of publication.

Publication bias remains a pressing concern in the academic community, limiting research findings' credibility, transparency, and generalizability – namely, there is a bias toward publishing only research with significant results. Research with non-significant results is difficult to publish in scientific journals. This means that the evidence published in scientific journals is biased towards studies that find effects. However, there is often significance in knowing of non-significant findings. The result is that academic outlets are filled with research that reports significance. This special issue aims to address publication bias in computational information science research.

Information Processing & Management is pleased to announce a special issue to provide computational information science research with a resource to publish research that does not reach statistical significance. The scope of the special issue is the same as that of Information Processing & Management (see Aims & Scope on the IP&M website), with the caveat that research published in this special issue must have non-significant results.

Guest editors:

Dr. Jim Jansen,

Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.