Data is the foundation of every AI system, shaping how models learn, behave, and make decisions. Yet without clear rules and shared responsibility, data practices can quickly undermine trust, transparency, and accountability. Our upcoming ELOQUENCE Webcafé will focus on what it takes to build responsible AI through robust data governance.
This session will explore how governance frameworks, ethical principles, and practical data management strategies help ensure AI systems remain trustworthy, compliant, and aligned with societal values throughout their lifecycle.
Meet Our Speakers
Anastasiya Kiseleva is Privanova’s Senior Ethics, Regulatory & Policy Lead, providing expertise on legal and ethics matters in EU projects and beyond. Anastasiya has an extensive background in both research and industry with a strong focus on trustworthy and responsible AI. She holds PhD degrees in two disciplines (law and computer science) in two countries (Belgium and France) with the research advancing transparency of AI. She was also recognised in the 2025 “Rising Stars of AI Ethics” by Women in AI Ethics.
With expertise in AI policy, data governance, privacy, and information technologies law, she contributes to multiple EU projects and academic research. She is a member of the Editorial Board of the European Health & Pharmaceutical Law Review, also acts as an AI Policy Expert at EUMASS and a researcher at Vrije Universiteit Brussels.
Aayushi Gupta is an experienced project manager skilled in client advisory, technology and business consulting, cross functional collaboration and business development. With extensive experience in managing EU commission projects, she excels in submitting new proposals under H2020, facilitating communication between EU commission and consortium and ensuring timely delivery of project milestones. Her educational background includes MBA specialising in Strategy and Marketing and a bachelor’s degree in Electronics and Communication engineering.
Together, Anastasiya and Aayushi will discuss how data governance, regulatory frameworks and practical implementation strategies can be aligned to support responsible AI, which data-related risks most commonly challenge current AI systems, and how ongoing work can help bridge the gap between policy principles and real-world AI practice.
- Date: January 27, 2026
- Time: 14:00 CET
- Registration link
Join us as we explore the key challenges, practical solutions, and the road ahead for data governance in responsible AI.
