Your work underscores the importance and supports the development of human-centric AI. What are some of the specific ways Ashoka’s flagship interdisciplinary research ecosystem equips academicians to approach data and AI responsibly with this lens?

Answered on: September 8, 2025
Answered by:
Partha Pratim Das Partha Pratim Das, Ph.D. Professor, Department of Computer Science and Director, Center for Data Science and Analytics Ashoka University
Answer

At Ashoka University, we believe that human-centric AI begins with cultivating interdisciplinary empathy and purpose. Our flagship program, Data Science for Social Impact, offered by CDSA (Centre for Data Science and Analytics), equips mid-career professionals with the skills to tackle real-world challenges in climate and health using data responsibly.

The program balances technical training with contextual sensitivity. Learners work with real datasets – on air quality, heatwaves, disease spread – while engaging with questions of equity, ethics, and community inclusion. Our custom-built datalake supports responsible research by providing curated, versioned datasets with full provenance tracking.

We integrate live policy and research contexts through collaborations with Ashoka’s interdisciplinary centres. Learners have worked with CHART (Centre for Health Analytics Research and Trends) on environmental health, ACPET (Ashoka Centre for a People-centric Energy Transition) on energy transitions, KCDH-A (Koita Centre for Digital Health at Ashoka) on public health data for conditions like diabetes, and ICPP (Isaac Centre for Public Policy) on public policy. These engagements ground data science not just in algorithms, but in lived experiences.

Many of our alumni are now applying these principles in their organizations—rethinking dashboards, redesigning surveys, or resisting biased models. Their journeys show that when data practitioners are taught to ask “who is missing?” before “what is the prediction?”, AI becomes a tool for inclusive change. At Ashoka, we are proud to nurture a generation of data professionals who see responsibility not as a constraint – but as a design principle.


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