Consistent Academic Support
Science Net ensures that research activities continue without interruption in the current global situation. Participants can engage through digital and hybrid conference formats.
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Conference Program
Session Tracks
Aligned with
UN Sustainable Development Goals
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.
Why it matters
SDG 4 — Quality Education
SDG 5 — Gender Equality
SDG 9 — Industry, Innovation and Infrastructure
SDG 10 — Reduced Inequalities
SDG 16 — Peace, Justice and Strong Institutions
Explore
All Session Tracks
Track 01
Ethical Frameworks in AI Development
This track explores the various ethical frameworks that guide the development and deployment of artificial intelligence systems. Participants will discuss the implications of these frameworks on innovation and societal impact.
Track 02
Transparency and Accountability in Machine Learning
This session focuses on the importance of transparency and accountability in machine learning algorithms. It aims to address the challenges and solutions related to ensuring that AI systems can be audited and held accountable.
Track 03
Bias Mitigation Strategies in Data Science
This track examines the methodologies and practices for identifying and mitigating bias in data science projects. Discussions will include case studies and best practices for achieving fairness in AI.
Track 04
Explainable AI: Bridging the Gap Between Models and Users
This session delves into the significance of explainable AI in enhancing user trust and understanding of machine learning models. It will cover techniques for improving model interpretability and user engagement.
Track 05
Governance of AI Systems: Policies and Regulations
This track addresses the governance structures necessary for the ethical oversight of AI systems. Participants will explore current policies and propose frameworks for effective regulation.
Track 06
Privacy-Preserving Machine Learning Techniques
This session focuses on innovative approaches to ensure privacy in machine learning applications. Discussions will include differential privacy, federated learning, and other privacy-preserving methodologies.
Track 07
Responsible AI: Balancing Innovation and Ethics
This track examines the concept of responsible AI and its implications for technological advancement. Participants will discuss strategies for aligning AI innovation with ethical considerations.
Track 08
Trust in Data Science: Building Credibility and Reliability
This session explores the factors that contribute to trust in data science practices and outputs. It will address the role of data quality, transparency, and ethical considerations in building credibility.
Track 09
Societal Impact of AI: Opportunities and Challenges
This track investigates the broader societal implications of AI technologies. Participants will discuss both the potential benefits and the ethical dilemmas posed by AI in various sectors.
Track 10
Ethical Decision Making in AI Systems
This session focuses on the frameworks and methodologies for ethical decision making in AI applications. It will explore case studies that highlight the complexities of ethical dilemmas in AI.
Track 11
Algorithmic Fairness: Theoretical Foundations and Practical Applications
This track examines the theoretical underpinnings of algorithmic fairness and its practical implications in real-world applications. Discussions will include metrics for fairness and strategies for implementation.