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International Conference on Explainable AI and Data Science

1–2 Sep 2026 Seoul, South Korea Standard / Physical Participation
Listener registration
$199
virtual · $289 in person

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Certificate of participation plus e-proceedings, slides and resource materials to keep.

Supporting global research

Connect with researchers across 30+ countries advancing Artificial Intelligence,Data Science,Machine Learning.

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3. Cancellation & Refund Policy :

  • 3.1 Cancellation is only permitted before the issuance of an official invitation letter. Once issued, no refund is available.
  • Full refund: cancellations made at least 70 days before the conference, with a cancellation form submitted 60 days prior.
  • Partial refund: cancellations 60–30 days prior may incur administrative/documentation fees.
  • No refund: cancellations within 30 days of the event, though a credit voucher valid for one year may be issued for future participation.
  • If a participant is unable to attend the conference due to personal reasons, the registration fee is non-refundable. However, the paid amount will be credited toward attendance at any of our international conferences within one year from the original date of registration.
  • As the conference will be held in a hybrid format, the organizer reserves the right to conduct the event either in person or virtually. Please note that no refunds will be issued due to changes in the event format.
  • Virtual registrations are non-refundable; participants may receive credit toward a future Researchfora event.

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6. Transfer of Registration :

  • A paid registration may be transferred to another individual from the same institution, pending approval. Requests must be submitted via email with authorization and updated registration details.
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What you'll hear about

Session tracks covered across the event.

View all 11 tracks →
This track focuses on the latest developments in explainable AI, emphasizing novel approaches and methodologies that enhance model interpretability. Researchers are invited to present their findings on algorithms that improve transparency and trust in AI systems.
This session highlights practical applications of interpretable models across various domains, showcasing case studies that demonstrate their effectiveness. Participants will explore how these models can be integrated into real-world systems to facilitate decision-making.
This track examines the role of transparent algorithms in data science, focusing on techniques that promote understanding and accountability. Contributions should address the challenges and solutions related to algorithmic transparency.
This session investigates the integration of human feedback in AI systems, emphasizing the importance of human-in-the-loop approaches for enhancing explainability. Discussions will center on methodologies that effectively incorporate human insights into model training and evaluation.
This track delves into the intersection of causality and machine learning, exploring how causal inference can improve model interpretability. Researchers are encouraged to present studies that highlight causal relationships and their implications for AI.
This session addresses the ethical considerations surrounding AI and data science, focusing on fairness and bias mitigation strategies. Contributions should explore frameworks that ensure ethical compliance and promote equitable outcomes.
This track emphasizes the importance of model debugging in achieving explainability, presenting techniques that help identify and rectify issues in AI models. Participants will share insights on tools and methodologies that enhance model reliability.
This session explores existing frameworks and standards for explainability in AI, discussing their effectiveness and areas for improvement. Researchers are invited to propose new frameworks that address current gaps in the field.
This track focuses on ensuring decision transparency in AI systems, highlighting approaches that make decision-making processes understandable to users. Contributions should examine the implications of transparent decision-making for trust and accountability.
This session addresses the regulatory landscape surrounding AI, emphasizing the importance of compliance in fostering trustworthy systems. Researchers are encouraged to discuss strategies for aligning AI practices with regulatory requirements.
This track investigates innovative visualization techniques that enhance the explainability of AI models and data-driven insights. Participants will showcase tools and methods that facilitate the interpretation of complex model outputs.
What's Included

Benefits of Registering as Listener

Access to Conference Sessions

Attend keynote, plenary and parallel sessions.

Networking Opportunities

Connect with researchers, educators and scholars.

Certificate of Participation

Receive a digital certificate after participation.

Invitation Letter Support

Official letter support after confirmed registration.

Conference Kit / Materials

E-proceedings and conference resource materials.

Access to Keynote Sessions

Learn from leading experts across event themes.