Building on the mission of tackling interdisciplinary research questions that transcend traditional boundaries, the DSI is excited to announce the launch of three new research initiatives: AI for Climate (AICE), Data Ecology, and Complementary AI. “These research initiatives will advance innovative data-driven applications, models, algorithms, and platforms in underexplored cross-disciplinary fields. They will also play an important role in executing DSI’s mission by organizing research into interdisciplinary themes focused on high-impact outcomes that benefit society and foster a flourishing data science and AI research ecosystem,” said DSI faculty co-director Dan Nicolae, Elaine M. and Samuel D. Kersten, Jr. Distinguished Service Professor, Department of Statistics, Human Genetics, Medicine, and the College.

AI for Climate

The AI for Climate (AICE) research initiative is led by Pedram Hassanzadeh (Geophysical Sciences and Computational and Applied Math), Jiwen Fan (Argonne National Labs), Ian Foster (Computer Science and Argonne National Labs), Amir Jina (Public Policy), and Tiffany Shaw (Geophysical Sciences). AICE will provide interdisciplinary integration of state-of-the-art AI with fundamental domain knowledge to accelerate and transform climate research with a focus on both scientific advances and societal impacts. 

“An essential step in addressing climate change and extreme weather is integrating physics, computer science, math, statistics, economics, public health, and social sciences knowledge and techniques to innovate and adapt AI methods to develop physics-informed predictive foundation models and trustworthy datasets. AICE will be among the first of its kind at leading universities to bring another level of focus and expertise on climate physics, AI theory, socioeconomic impacts and adaptation/mitigation,” said Pedram Hassanzadeh, Associate Professor of Geophysical Sciences.

AICE will integrate the ongoing remarkable innovations in AI with fundamental domain knowledge around three interconnected thrusts: AI for 

1) climate physics and prediction; 2) climate change socioeconomic impacts; and 3) climate mitigation, adaptation, and engineering.

To get involved in the AICE research community and receive updates about upcoming events and funding opportunities, please sign up for the AICE email list. 

Data Ecology

The Data Ecology research initiative is co-led by Raul Castro Fernandez (Computer Science) and Bridget Fahey (Law) and will advance the principles, theory, methodologies, algorithms, and systems to design and control dataflows. Data shapes our cultural, economic, and social environments but is not fixed and flows from those who collect it to those who apply it to increasingly varied ends.

“Our goal is to develop finely calibrated ways of sharing data that can avoid over- and under-sharing, maximize value while minimizing harm, and increase opportunities for stakeholders to exert control over data’s movements,” said Bridget Fahey, Assistant Professor of Law.  But, she added, “doing that requires cross-disciplinary systems integrating technical, legal, social, and economic approaches.”

The term “data ecology” is designed to capture that kind of cross-disciplinary study of data environments and the interventions that can improve their performance. The initiative will explore topics related to why different agents pursue data; how to value data; what legal-technical systems can maximize control over data sharing; and more.

The Data Ecology research initiative supports cross-disciplinary research, hosts scholarly convenings, engages public and private thinkers, and develops models of student instruction about data production, data movement, and data regulation.

Complementary AI

The faculty co-leads of the Complementary AI research initiative are James Evans (Sociology) and Chenhao Tan (Computer Science). The notion of an AI that computationally mimics human intelligence has dominated the design of computational intelligence, but is tuned to replace humanity rather than augment its capacity. Rather than targeting Artificial General Intelligence (AGI), Evans and Tan argue that AI should be designed to complement individual and collective human capacity, avoid humanity’s blind spots, overcome cognitive and cultural limitations, and unleash novel senses, skills, and imagination.

“While the rest of the world focuses on bracing for new AI to come or criticizing AI, we believe that it is critical to promote collective discussion regarding the best route for human-AI complementarity and set complementary AI as the ultimate vision, competing with and (hopefully) displacing AGI,” explains Chenhao Tan, Assistant Professor of Computer Science and Data Science.

The initiative will draw on business and economics expertise to measure and model human-AI productivity; legal and policy expertise to propose models of AI co-regulation; and expertise across sciences, engineering, arts, and humanities to explore and experiment with expanding human-AI capacity and imagination regarding the greatest challenges facing humanity.

To get involved in the Complementary AI research community and receive updates about upcoming events and opportunities, please sign up for the Complementary AI email list.