HYBRID EVENT: You Can Participate In Person At Bayamon, Puerto Rico Or Virtually From Your Home Or Work
ICEAIDS · Registering as Listener

International Conference on Explainable AI and Data Science

28–29 Jun 2027 Bayamon, Puerto Rico Standard / Physical Participation
Listener Registration
$130
virtual · $155 in person
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Supporting global researchConnect with researchers across 30+ countries

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1. Policy on Cancellations & Refunds :

  • A full refund is possible only if the cancellation request is submitted at least 70 days before the conference, with formal paperwork completed at least 60 days prior.
  • If cancellation occurs between 60 and 30 days before the event, partial refunds may be granted based on administrative costs incurred.
  • Cancellations made within 30 days of the event are non-refundable, though participants will receive a credit valid for one year.
  • Registrations made less than 30 days before the event are not eligible for refunds, but may be transferred to another ISIT conference.
  • Since this conference is 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 registration fees are non-refundable. A credit will instead be issued for use at a future conference event.

2. Participation & Registration Requirements :

  • To attend an ISIT event, participants must complete the registration process within the stipulated time.
  • The event schedule, venue, and format are subject to changes at the discretion of the organizers, with prior notice sent via email.
  • ISIT is not liable for any financial losses resulting from changes in event details.
  • Fees paid for registration are strictly non-refundable.
  • If the primary author cannot participate, a co-author may attend instead, but refunds will not be granted for non-attendance.

3. Submission & Publication Norms :

  • Researchers submitting papers to ISIT must ensure their institution or supervisor is aware of their submission.
  • Each submission is subject to a rigorous peer review before being accepted for presentation.
  • Only papers linked to a completed registration will be included in conference proceedings.
  • The submitting author is considered the primary author; ISIT does not verify individual authorship claims.
  • If any concerns regarding authorship arise and are validated, the paper will be withdrawn without reconsideration.
  • Once a paper is removed, it cannot be reintroduced into any ISIT publication.

4. Travel & Accommodation Responsibilities :

  • Attendees are responsible for making their own travel and lodging arrangements.
  • ISIT does not provide logistical assistance for travel or accommodation.
  • The organization bears no responsibility for expenses incurred due to conference modifications or rescheduling.

5. Visa & Invitation Letter Policy :

  • Attendees are responsible for making their own travel and lodging arrangements.
  • ISIT does not provide logistical assistance for travel or accommodation.
  • The organization bears no responsibility for expenses incurred due to conference modifications or rescheduling.
  • ISIT does not engage directly with consulates or embassies on behalf of attendees.
  • Participants must handle their own visa applications and processes.
  • Important Information Regarding Invitation Letters:
    • The invitation letter is provided solely for conference attendance and does not serve as a document for immigration, employment, or residency purposes.
    • The letter assists in visa applications but does not guarantee visa approval.
    • ISIT holds no responsibility for visa denials or processing delays, and all related costs are borne by the applicant.
    • Any alterations or unauthorized use of the invitation letter will result in its invalidation and possible cancellation of conference participation.
    • Legal action may be pursued if the document is misused.
    • By accepting the invitation letter, attendees agree to comply with international travel regulations and ethical participation standards.

6. Registration Transfers :

  • Registrations may be transferred to another individual from the same institution if the original participant cannot attend.
  • Transfer requests must be made via email to [email protected] with necessary details and supporting documents.
  • Transfers must be requested at least 14 days before the event; otherwise, they will not be accommodated.
  • Transferred registrations are not eligible for refunds.

7. General Considerations :

  • Any modifications or cancellations must be communicated in writing to [email protected].
  • Registration confirms acknowledgment and acceptance of these policies.
  • ISIT does not initiate automatic transactions; all payments are voluntarily completed by registrants.
  • Once registered, participants must submit a conference registration form within three days for confirmation.
  • Travel plans should only be finalized after receiving the official conference itinerary, which will be shared 15 days before the event.

8. Conference Programme and Participation Policy :

  • Presentations from different but relevant academic areas may be combined by the Organiser within an interdisciplinary or multidisciplinary programme to encourage cross-disciplinary understanding, interaction, and future research partnerships.
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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.