AIIM2024 welcomes high-quality, original, and previously unpublished submissions in the theories, technologies, and applications on all aspects of artificial intelligence and information management.

Paper submission must be in English. All papers will be double-blind and reviewed by the Program Committee based on technical quality, relevance to data mining, originality, significance, and clarity. All paper submissions will be handled electronically. Papers that do not comply with the Submission Policy will be rejected without review.

Each submitted paper must include an abstract of up to 200 words and be no longer than 12 single-spaced pages with 10pt font size (including references, appendices, etc.). All papers must be submitted electronically through the CMT paper submission system in PDF format only. Supplementary material may NOT be submitted as a separate PDF file, and reviewers are not obligated to consider this, and your manuscript should, therefore, stand on its own merits without any supplementary material. Supplementary material will not be published in the proceedings.

We require that any submission to AIIM must not be already published or under review at another archival conference or journal. Submitting a paper to the conference means that if the paper was accepted, at least one author will complete the regular registration and attend the conference to present the paper. For no-show authors, their papers will NOT be included in the proceedings.


Double Blind Review

Paper submissions must adhere to the double-blind review policy.
Submissions must remove all details identifying the author(s) from the original manuscript (including the supplementary files, if any), and the author(s) should refer to their prior work in the third person and include all relevant citations.
​The author list and order cannot be changed after the paper is submitted.

All manuscripts must be prepared and submitted in accordance with the above format. Usage of other formats may lead to the disqualification of the paper for the conference.
All submitted papers will be reviewed by three reviewers.



Broad Scope:
​
Artificial Intelligence: AI Logics, Deduction, Learning, Problem Solving - Cognitive Science and Technologies - Computational Intelligence - Agent Technology - Natural Language Processing - Knowledge Processing

Explainable AI
AI Ethics
AI in Healthcare
AI for Natural Language Processing
AI for Autonomous Vehicles
Reinforcement Learning
AI in Robotics
AI for Social Good
Generative AI
AI for Smart Cities
AI in Financial Services
AI for Computer Vision
AI in Education
AI for Cybersecurity
AI in Natural Language Generation
AI for Recommender Systems
AI in Human-Robot Interaction
AI for Energy Optimization
AI for Agriculture
AI in Drug Discovery

AI for Data Analytics and Decision-Making
AI in Natural Language Understanding
AI for Personalized Healthcare
AI in Human Resources and Talent Management
AI for Social Robotics and Assistive Technologies
AI for Autonomous Systems
AI in Internet of Things (IoT) Applications
AI for Privacy and Security
AI for Virtual and Augmented Reality
AI in Sports Analytics
AI in Supply Chain Management
AI for Fraud Detection in Insurance
AI in Energy Grid Optimization
AI for Natural Disaster Prediction and Response
AI for Personalized Marketing
AI in Financial Risk Management
AI for Natural Language Generation in Journalism
AI in Environmental Monitoring and Conservation
AI for Personalized Learning in Education
AI in Legal Research and Document Analysis

Topics include but not limited to the following:
AI-Focused Topics in Information Management
Topics (include but not limited to) that belong to the intersection of AI and social sciences:

Ethical considerations in AI: Examining the ethical implications of AI systems, such as bias, privacy, and transparency, and their impact on society and social values.
Algorithmic fairness and bias: Investigating the fairness and potential biases in AI algorithms, particularly regarding issues of race, gender, and socioeconomic status.
Social impact of AI: Analyzing how AI technologies are reshaping various aspects of society, including employment, education, healthcare, and governance.
Human-computer interaction: Studying the interaction between humans and AI systems, including user experience, trust, and the psychological and social effects of interacting with intelligent machines.
AI and social inequality: Investigating the potential impact of AI on existing social inequalities and exploring strategies to mitigate them.
AI and decision-making: Exploring how AI systems influence decision-making processes in various domains, such as criminal justice, finance, and public policy, and examining the implications for fairness and accountability.
AI in social research: Utilizing AI techniques for social research purposes, such as analyzing large-scale social media data, sentiment analysis, and social network analysis.
AI and labor market dynamics: Examining the effects of AI on employment, job displacement, and the changing nature of work, as well as exploring policies and strategies for adapting to these changes.
AI and human values: Exploring the alignment between AI systems and human values, and investigating how AI can be designed to reflect and promote societal values.
AI and human behavior: Investigating how AI technologies influence human behavior, attitudes, and social interactions, and studying the psychological and sociological implications.
AI in education: Examining the potential of AI for personalized learning, intelligent tutoring systems, and educational assessment, as well as considering the ethical and equity concerns associated with the use of AI in education.
AI and privacy: Investigating the privacy challenges posed by AI technologies, such as data collection, surveillance, and the use of personal information for targeted advertising or decision-making.
AI and social networks: Analyzing the role of AI in social network analysis, community detection, and understanding online behavior and dynamics in social media platforms.
AI and cultural implications: Exploring how AI systems interact with different cultural contexts, norms, and values, and examining the cultural impact of AI on societies and communities.
AI and political implications: Investigating the role of AI in political processes, such as opinion mining, political polarization, and the impact of algorithmic news curation on political discourse.


This list is not exhaustive, but it provides a broad range of topics that lie at the intersection of AI and social sciences.
Topics include but not limited to the following:
Track 1: Intelligent Information Management

Machine Learning for Information Extraction
Natural Language Processing for Text Classification
Intelligent Information Retrieval
Knowledge Graphs for Knowledge Management
Recommender Systems for Personalized Information Delivery
Data Quality Management
Deep Learning for Image and Video Analysis
Intelligent Document Management
Data Integration and Fusion
Explainable AI for Transparent Decision Making
Document Understanding
Deep Reinforcement Learning for Intelligent Information Systems
Data Governance and Compliance
Cognitive Search and Knowledge Discovery
Explainable AI for Trustworthy Information Management
Data Integration and Data Wrangling
Information Security and Privacy
Knowledge Graph-based Recommendation Systems
Data Visualization and Visual Analytics
Intelligent Data Governance and Metadata Management
Intelligent Information Retrieval in Big Data
Data Privacy Preservation
Predictive Analytics for Information Management
Information Visualization
Data Governance in Multi-cloud Environments
Personalized Information Access and Recommendation
Knowledge Discovery in Scientific Research
Data Privacy in Healthcare
Data Classification and Categorization
Intelligent Data Analytics in Financial Markets

Knowledge Management Systems
Data-driven Decision Support Systems
Text Analytics and Sentiment Analysis
Intelligent Data Curation and Preservation
Data Quality Assessment and Improvement
Chatbots for Customer Service
Recommendation Systems for Information Discovery
Automated Information Extraction from Social Media
Data Analytics for Business Process Optimization
Data Governance and Compliance in the Cloud
Intelligent Information Integration
Information Security and Threat Detection
Intelligent Search and Information Retrieval
Smart Data Governance and Ethics
Intelligent Document Automation
Data Governance for the Internet of Things (IoT)
Smart Content Management
Data Exploration and Visualization
Intelligent Data Integration in Healthcare
Data Privacy in Social Networks
Intelligent Knowledge Discovery
Information Extraction from Multimedia Sources
Intelligent Data Governance in Financial Services
Data-driven Decision Support in Supply Chain Management
Intelligent Data Governance in the Era of Big Data
Text Mining and Information Extraction
Intelligent Recommendation Systems in E-commerce
Intelligent Data Visualization for Decision Making
Intelligent Data Governance in Smart Cities
Data Governance for Ethical AI

Track 2:
Smart Technical Communication
Track 3:
Intelligence in Business Analytics
Topics include but not limited to the following:

Natural Language Processing for Technical Documentation
Chatbots for Technical Support
Automated Content Generation
Information Retrieval for Technical Knowledge
Translation and Localization of Technical Content
Adaptive User Assistance
Quality Assurance for Technical Communication
Visual Communication in Technical Documentation
Collaborative Authoring and Content Management
Content Recommendation for Technical Users
Intelligent Content Management Systems
Voice User Interfaces for Technical Documentation
Intelligent Technical Training and Education
Content Localization and Multilingual Communication
Automated Documentation Compliance
Information Extraction from Technical Data
Intelligent Technical Search and Information Retrieval
Augmented Reality for Technical Communication
Cognitive Automation in Technical Documentation
Sentiment Analysis and User Feedback in Technical Communication
Automated Technical Writing
Content Personalization in Technical Communication
Intelligent Information Design
Natural Language Generation for Technical Communication
Automated Quality Assessment of Technical Content
Chatbots for Technical Authoring Assistance
Intelligent Content Localization Workflow
Data Visualization for Technical Communication
Intelligent Content Delivery Platforms
​Speech Recognition and Transcription for Technical Communication

Predictive Analytics
Recommender Systems
Customer Segmentation and Personalization
Sentiment Analysis and Opinion Mining
Fraud Detection and Risk Management
Natural Language Processing for Text Analytics
Supply Chain Optimization
Customer Experience Analytics
Pricing and Revenue Optimization
Marketing Analytics
Dynamic Pricing Strategies in E-commerce
Recommendation Systems for Cross-Selling and Up-Selling
Social Media Analytics and Influencer Marketing
Market Basket Analysis and Association Rule Mining
Customer Lifetime Value Prediction and Retention Strategies
Demand Forecasting and Inventory Optimization
Fraud Detection and Anti-Money Laundering
Sales and Revenue Analytics
Sentiment Analysis and Brand Perception
Credit Scoring and Risk Assessment
Customer Segmentation and Targeted Marketing
Sales Forecasting and Demand Planning
Fraud Detection in Financial Transactions
Sentiment Analysis for Brand Reputation Management
Social Network Analysis and Influencer Identification
Price Optimization and Revenue Management
Supply Chain Analytics and Optimization
Customer Journey Analytics
Risk Management and Portfolio Optimization
​Text Analytics for Market Research

Aizuwakamatsu is a city on Japan’s Honshu island. In the center, towering white Tsuruga Castle has distinctive red-tiled roofs. The surrounding park is known for spring cherry blossoms. Aizu Bukeyashiki is the reconstructed residence of an Edo-era samurai family. Nearby, a traditional teahouse sits in Oyakuen Medicinal Gardens. To the east, vast Lake Inawashiro, overlooked by Mount Bandai, is home to swans in winter.