Social computing refers to human interactions via and with computing technologies, where humans actively provide inputs to influence computations and where computational outcomes shape individual lives and social groups. Computational social science is concerned with taking computational approaches to social sciences, particularly using computational methods to model, simulate, and analyse social phenomena. Through user studies, analysis of large datasets, and design and deployment of new systems, these emerging fields seek to understand and influence the behavior of these systems and their users.
Groups and Researchers in this Field
Algorithms & Inequality
Rediet Abebe is a junior fellow at the Harvard Society of Fellows and an Andrew Carnegie Fellow. Her research examines the interaction of algorithms and inequality, with a focus on contributing to the scientific foundations of this area. Abebe has also co-founded numerous organizations, including the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), and the associated international research initiative. Abebe is the recipient of numerous awards and honours, including the Hector Endowed Fellowship by the European Laboratory for Learning and Intelligent Systems (ELLIS), MIT Technology Fellows 35 Innovators under 35, the ACM SIGKDD Dissertation Award, and an honorable mention for the ACM SIGecom Dissertation Award. Abebe is currently leading several large-scale evaluations of ML systems used in commercial, legal, and policy contexts. Read more
Meeyoung Cha is a scientific director of MPI-SP in Bochum, Germany. Her interests include data science and computational social science, with a focus on understanding social information and human-machine interactions. Meeyoung’s research on misinformation, poverty mapping, fraud detection, and long-tail content has received wide citations and best paper awards. She is the recipient of the Korean Young Information Scientist Award 2019, the AAAI ICWSM Test-of-Time! Award 2020, and the ACM IMC Test-of-Time Award 2022. Prior to joining MPI, Meeyoung was a chief investigator at IBS (2019-current), a faculty member at KAIST (2010-current), a visiting professor at Facebook (2015-2016), and a postdoctoral researcher at MPI-SWS (2008-2010). She received her Ph.D. in computer science from KAIST in 2008. Read more
Manuel Gomez Rodriguez is interested in developing machine learning and large-scale data mining methods for analysis and modeling of large real-world networks and processes that take place over them. His research comprises several dimensions: developing models of these networks and processes, assessing their theoretical properties and limitations; developing machine learning algorithms to fit the models and computational methods to influence processes over networks; and validating models and methods on gigabite- and terabyte-scale real-world datasets. Ultimately, he aims to provide computational tools with applications in a variety of domains, e.g. social and information sciences, economics, decision theory, causality, and epidemiology. Read more
Krishna Gummadi heads the Social Computing research group at the Max Planck Institute for Software Systems. He is broadly interested in understanding and building networked and distributed computer systems. Currently, the group's research focuses on social computing systems: an emerging class of societal-scale human-computer systems that facilitate interactions and knowledge exchange between individuals, organizations, and governments in our society. A few examples include social networking sites like Facebook, blogging and microblogging sites like LiveJournal and Twitter, and content sharing sites like YouTube, among many others. Through user studies, examining data, and building systems, the group aims to understand, predict, and control the behavior of their constituent human users and computer systems. Read more
Moritz Hardt is a scientific director at the Max Planck Institute for Intelligent Systems, where he leads the Social Foundations of Computation Department. His research contributes to the scientific foundations of machine learning and algorithmic decision making with a focus on social questions. His research interests span four areas: (1) Applying machine learning in social and economic contexts, (2) formulating social and dynamic actions as mathematical models, (3) examining the validity and reliability of statistical methods and the construction of datasets within scientific communities, and (4) the pursuit of normative goals, and in particular, how to formulate values and norms mathematically. Hardt is co-founder of the conference "Fairness, Accountability, and Transparency in Machine Learning." He is co-author of "Fairness and Machine Learning: Limitations and Opportunities" (MIT Press, 2022) and "Patterns, Predictions, and Actions: A Story About Machine Learning" (Princeton University Press, 2022). Read more
Ksenia Keplinger leads the independent research group “Organizational Leadership & Diversity” at the Max Planck Institute for Intelligent Systems in Stuttgart. Prior to this, she first was a postdoctoral researcher and then a faculty member at the University of Colorado Boulder, USA. She earned her Ph.D. in Business Studies at the Johannes Kepler University of Linz, Austria, in 2016.
The overarching research goal of her "Organizational Leadership & Diversity” group is to support organizational leaders in using artificial intelligence to unleash the true potential of diversity and inclusion. In particular, her research group uses a variety of research methods (qualitative, quantitative, computational simulations, and NLP) to explore the nature of leadership in the artificial intelligence age and to develop ways to mitigate bias in human-machine partnership. On a larger scale, her research aims to help leaders and organizations clarify how they can contribute to a more tolerant, diverse, and inclusive society. Read more
Celestine Mendler-Dünner is a research group leader at MPI-IS, a Principal Investigator at the ELLIS Institute Tübingen, and a faculty member of the Tübingen AI Center. Her research spans machine learning, prediction and algorithmic decision-making with a focus on the societal embedding of technology, broadly scoped. She pursues theoretical as well as empirical questions that shed light on how data-driven systems interact with society, and how to build reliable systems in dynamic environments. She obtained her PhD from ETH Zurich in computer science and before moving to Tübingen she spent two years as a SNSF postdoctoral fellow at UC Berkeley. Her research contributions have been recognized with the ETH Medal, the Fritz Kutter Prize and the IBM Eminence and Excellence award. She is a fellow of the Elisabeth Schiemann Kolleg, and a member of the Tübingen Cluster of Excellence on ML for Science. Read more
Abraham Mhaidli is a Research Group Leader at MPI-SP. His research interrogates the harms of current and emerging technologies, asking: (1) what are the ethical, consumer, and societal harms of technologies; (2) how can they be designed so as to not cause harm; and (3) what are techniques we can use to better understand the technologies and harms that are to come. Abraham's work has been recognized by best paper awards and honorable mention at CHI, PETS, and SOUPS. Abraham completed his PhD in Information at the University of Michigan in 2023.
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