As computers increasingly make decisions about who gets a loan, a job, or even bail, the expansion of AI algorithms has provoked public concern about ethical issues, and the need to understand what constitutes AI algorithms and how they make decisions becomes ever more pressing. For example, an increasing number of high-profile news outlets have widely reported that widely-used algorithms have unfairly discriminated against some groups of people (e.g., by gender and race) in parole decisions and other major life events. Focusing more attention on ethical bias in learning algorithms is key to unlocking the potential of automated decision systems while ensuring fairness and accountability so that everyone can advance equally in society.

Ethical AI has become increasingly important, and it has been attracting attention from academia and industry due to its increased popularity in real-world applications with fairness concerns. It also places fundamental importance on ethical considerations in determining legitimate and illegitimate uses of AI. Organizations that apply ethical AI have clearly stated, well-defined review processes to ensure adherence to legal guidelines. Therefore, the wave of research at the intersection of ethical AI in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science.

Important Dates

The following are the proposed important dates for the workshop. All deadlines are due 11:59 pm Pacific Time.

• Paper Submission: April 30th, 2026

• Paper Notification: June 4th, 2026

• Workshop Date: TBA

Topics of Interest 

We encourage submissions in various degrees of progress, such as new results, visions, techniques, innovative application papers, and progress reports under the topics that include, but are not limited to, the following broad categories:

• Algorithmic fairness and bias in classifying and clustering big data

• Human-in-the-loop for ethical-aware machine learning

• Ethical recommender systems and diversity in recommendations

• Learning an ethical-aware representation on heterogeneous data domains

• Causality-based fairness in high-dimensional data

• Integration of observation for causality-based bias control

• Preserving fairness in graph embedding

• Novel visualization techniques to facilitate the query and analysis of data bias

• Robustness and generalization of LLMs

• Bias mitigation and the fairness of LLMs

• Explainability, interpretability, privacy, and security of LLMs

• First-hand experience creating or working with company practices for ethical AI

• Ethical considerations in high-performance computing (HPC)

• Philosophical theories and their implications for AI ethics

And with a particular focus, but not limited to, these application domains:

• Application of ethical AI methods in large-scale data mining

• Computer vision (fairness in face recognition, object relation, debiasing in image processing, and video)

• Natural language processing (fair text generation, semantic parsing)

• Reinforcement learning (fairness-aware multi-agent learning, compositional imitation learning)

• Social science (racial profiling, institutional racism)

Submission Guidelines

Submissions are limited to a total of 5 pages, including all content and references. There will be no page limit for supplemental materials. All submissions must be in PDF format and use ACM Conference Proceeding templates (two-column format). One recommended setting for a Latex file of an anonymous manuscript is: \documentclass[sigconf, anonymous, review]{acmart}. Template guidelines are here: https://www.acm.org/publications/proceedings-template.

Following this KDD conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well-executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible.

Submit your paper through the EAI workshop CMT submission site: https://cmt3.research.microsoft.com/EAI2026 

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support. 

Paper Acceptance

Accepted workshop papers will be categorized as either poster or oral presentations. Both types will be posted on the workshop website, but will NOT be included in the official KDD proceedings.

Upon notification, we ask that authors of accepted works deanonymize their papers, make any final changes, and then submit a camera-ready version to the CMT submission site. The workshop website will then be updated with links to accepted papers. Note that accepted works will not be formally published. This means that:

• Authors can retain full copyright of their works.

• Work contained in accepted papers is not precluded from being published in other research venues.

• Submitted papers are allowed to have significant overlap with previously published or currently submitted work (in this case, please indicate overlapping works).

The workshop chairs and committees will designate one Best Paper Award and one Runner-Up Paper Award for accepted oral papers. Additionally, the workshop organizers encourage all authors of accepted papers to extend their work and submit to the special issue in the Journal of Frontiers in Big Data: Ethical Artificial Intelligence: Methods and Applications.

Any questions may be directed to the email address: chen_zhao@baylor.edu

Attendence

For each accepted paper, at least one author must attend the conference and present the paper.