Title: Human–AI Partnership in Data Systems: Inclusion by Design or Bias at Scale?
Time: Thursday, June 04th 2026
Location: TBD
Organizers: Maria D'Souza Choudhary, Donatella Firmani
Panelists:
Manish Gupta (Senior Director, Google DeepMind)
Rihan Hai (Assistant Professor at Web Information Systems (WIS) group of TU Delftand the lead of InfiniData Team.)
Sujaya Maiyya (Assistant professor at Cheriton School of Computer Science at University of Waterloo)
Paolo Missier (Professor of Computer science and Director of the Institute for Data and AI at University of Birmingham)
Felix Naumann (Head of Information Systems at HPI, Coordinator for the HPI Data & AI Cluster)
Yogesh Simmhan (Associate Professor in the Department of Computational and Data Sciences at the Indian Institute of Science)
Brit Youngmann (Assistant professor of Computer Science at the Technion)
The database community is entering a new phase where AI is becoming deeply embedded in data systems. From AI-assisted query optimization and data cleaning to automated knowledge extraction and scientific discovery, modern data systems are increasingly designed as human–AI collaborative environments. This transformation raises a critical question for the community:
Will AI-powered data systems expand participation in computing research, or will they amplify structural inequities already present in the ecosystem?
We identify four factors that already shape participation in the global data systems community and that will be impacted
T1- Research under reduced funding outside major research hubs.
T2- Data and compute concentration within large technology companies
T3- Disparities between researchers in the Global North and in the Global South
T4- Community Actions
What structural burdens do researchers at under-resourced institutions face in sustaining competitive research programs?
How can AI tools, shared infrastructure, and collaborative networks reduce funding and participation barriers for researchers outside major research hubs?
Can AI reduce barriers caused by career breaks, geography, and language — or will it reproduce historical inequities at scale?
Are we reaching a point where certain research problems are effectively inaccessible without industry-scale compute and proprietary datasets?
How can industry–academia collaborations be structured to maximize scientific openness, and what safeguards are needed to prevent corporate priorities from distorting research agendas?
LLMs are “aligned” by organizations — this may conflict with pluralism. How can open-source models and datasets contribute to democratizing access to advanced AI systems?.
How can AI-assisted collaboration, multilingual systems, and hybrid participation reduce structural inequities between researchers in the Global North and Global South?
Scientific participation should not depend entirely on institutional wealth or geography. Can we move from institution-owned infrastructure to community-owned infrastructure?
How can conferences and professional societies reduce participation barriers for researchers from emerging regions?
What role should organizations such as ACM play in advancing global inclusion?
How can conferences improve student and community participation ? Preconference workshops, Travel grants, Collaboration networks, Sustained mentorship and measurable DEI efforts?.
Should the community rethink reviewing models, publication expectations, or conference formats to lower participation barriers?
During an hour and a half session:
The moderator will provide the context and introduction to the panel for the audience.
Panelists will introduce themselves.
The moderator will promote a collective dialogue through questions to panelists about their point of view and experiences.
A final round will seek to drive conclusions by the panelists and the audience.