ADMA 2026 Special Session on Responsible Data Intelligence

Overview

Call for Papers

Responsible Data Intelligence (RDI) focuses on developing trustworthy, transparent, and reliable data-driven systems by integrating data mining, machine learning, and data management techniques.

As data-driven decision-making becomes increasingly pervasive, challenges such as fairness, robustness, interpretability, and data quality have become critical. This special session aims to bring together researchers working on data mining and AI methods that support responsible and trustworthy data intelligence across diverse domains.

Scope

Aims and Scope

Rapid advances in artificial intelligence, data science, and large-scale data systems have increased the importance of intelligent systems that are not only accurate but also trustworthy, transparent, and responsible. Data mining plays a central role in extracting insights from complex and large-scale data, while modern AI systems increasingly rely on high-quality data, robust data pipelines, and scalable data management infrastructures.

RDI is an emerging interdisciplinary paradigm that integrates data mining, data-driven AI, and data management to enable reliable, fair, and accountable decision-making across the entire data and AI lifecycle. The session welcomes contributions on algorithms, systems, data-driven AI methods, and real-world applications.

Topics

Topics of Interest

Topics include, but are not limited to, the following areas:

01Fairness, Accountability, Transparency, and Data Quality

Fairness and bias detection, evaluation, and mitigation
Explainability and interpretability of data mining and AI models
Transparency, accountability, and data and model provenance
Data quality assessment, data cleaning, and dataset curation
Data governance, auditing, and trustworthy evaluation of data-driven systems

02Robustness, Reliability, and Uncertainty-Aware Data Mining

Robust and reliable data mining algorithms
Uncertainty modeling and confidence estimation
Data mining under noisy, incomplete, or adversarial data
Reliability guarantees in large-scale and distributed data mining
Responsible data pipelines and end-to-end system reliability

03Responsible AI Systems and Foundation Models for Data Intelligence

Trustworthy large language models (LLMs) for data analytics
Retrieval-augmented generation (RAG) with grounding and reliability
AI-assisted data analytics with human-in-the-loop workflows
Agentic AI and responsible intelligent systems
Multimodal data mining with transparency and accountability

04Responsible Data Mining across Data Types and Modalities

Relational and tabular data mining with fairness and reliability considerations
Graph and network mining with accountability and trust
Knowledge graph mining with provenance and reliability
Spatial and spatiotemporal data mining under uncertainty
Temporal, streaming, and dynamic data mining with reliability guarantees

05Applications of Responsible Data Intelligence

Responsible finance and fintech analytics
Environmental, Social, and Governance (ESG) and sustainability analytics
Trustworthy healthcare and biomedical data science
Responsible smart cities and urban computing
Ethical and reliable industrial and business intelligence

Submission

Submission Guidelines

Papers should be submitted via the official ADMA 2026 submission system. Authors should select the special session track Responsible Data Intelligence (RDI) during submission.

Manuscripts should follow the ADMA 2026 main conference submission guidelines, including format, page limit, and policy requirements.

Submit via CMT

Committee