Timezone: »
Computational social science is an emerging academic research area at the intersection of computer science, statistics, and the social sciences, in which quantitative methods and computational tools are used to identify and answer social science questions. The field is driven by new sources of data from the Internet, sensor networks, government databases, crowdsourcing systems, and more, as well as by recent advances in computational modeling, machine learning, statistics, and social network analysis.
The related area of social computing deals with the mechanisms through which people interact with computational systems, examining how and why people contribute to crowdsourcing sites, and the Internet more generally. Examples of social computing systems include prediction markets, reputation systems, and collaborative filtering systems, all designed with the intent of capturing the wisdom of crowds.
Machine learning plays in important role in both of these research areas, but to make truly groundbreaking advances, collaboration is necessary: social scientists and economists are uniquely positioned to identify the most pertinent and vital questions and problems, as well as to provide insight into data generation, while computer scientists contribute significant expertise in developing novel, quantitative methods and tools.
The inaugural workshop brought together experts from fields as diverse as political science, psychology, economics, and machine learning, connecting researchers with common goals but disparate methods and audiences. The quality of work presented was excellent and we expect the same caliber of submissions again this year. As with last year's workshop, we hope to attract a mix of established members of the NIPS community and researchers who have never attended NIPS and will provide an entirely new perspective.
The primary goals of the workshop are to provide an opportunity for attendees to meet, interact, share ideas, establish new collaborations, and to inform the wider NIPS community about current research in computational social science and social computing. To this end, the workshop will consist of invited talks, contributed talks, a poster session, a panel session, and a dinner.
We intend for the workshop to be broad enough to cover a wide variety of problems and computational techniques. Consequently, we plan to include research on theoretical models, empirical work, and everything in between, including but not limited to:
* Automatic aggregation of opinions or knowledge
* Incentives in social computation (e.g., game-theoretic approaches)
* Prediction markets / information markets
* Studies of events and trends (e.g., in politics)
* Quality control for user generated content
* Analysis of and experiments on distributed collaboration and consensus-building, including crowdsourcing (e.g., Mechanical Turk) and peer-production systems (e.g., Wikipedia and Yahoo! Answers)
* Group dynamics and decision-making
* Modeling network interaction content (e.g., text analysis of blog posts, tweets, emails, chats, etc.)
* Social networks
* Games with a purpose
The workshop will address the following specific goals:
* Identify and formalize open research areas.
* Propose, explore, and discuss new questions and problems.
* Discuss how best to facilitate the transfer of research ideas between the computer and social sciences.
* Direct future work and create new application areas, novel modeling approaches, and unexplored collaborative research directions.
The workshop will be announced via email and relevant mailing lists (including the ML-NEWS, UAI, COLT, PASCAL, and topic modeling lists). We will also ask the members of our interdisciplinary program committee (currently being formed) to spread the word in their own research communities. We will construct a workshop website, containing information for prospective participants and pointers to relevant research within the computer and social sciences. Accepted submissions will be made publicly available on the website.
Author Information
Winter Mason (Stevens Institute)
Jennifer Wortman Vaughan (Microsoft Research)

Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to human-centered approaches to transparency, interpretability, and fairness in machine learning as part of MSR's FATE group and co-chair of Microsoft’s Aether Working Group on Transparency. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.
Hanna Wallach (MSR NYC)
More from the Same Authors
-
2021 : GAM Changer: Editing Generalized Additive Models with Interactive Visualization »
Zijie Jay Wang · Harsha Nori · Duen Horng Chau · Jennifer Wortman Vaughan · Rich Caruana -
2022 : Generation Probabilities are Not Enough: Improving Error Highlighting for AI Code Suggestions »
Helena Vasconcelos · Gagan Bansal · Adam Fourney · Q.Vera Liao · Jennifer Wortman Vaughan -
2022 : Beyond Decision Recommendations: Stop Putting Machine Learning First and Design Human-Centered AI for Decision Support »
Zana Bucinca · Alexandra Chouldechova · Jennifer Wortman Vaughan · Krzysztof Z Gajos -
2022 : Panel »
Meena Jagadeesan · Avrim Blum · Jon Kleinberg · Celestine Mendler-Dünner · Jennifer Wortman Vaughan · Chara Podimata -
2021 : Fairness:: Assessing Fairness in Practice: AI Teams’ Processes, Challenges, and Needs for Support »
Michael Madaio · Hariharan Subramonyam · Jennifer Wortman Vaughan -
2021 Panel: How Should a Machine Learning Researcher Think About AI Ethics? »
Amanda Askell · Abeba Birhane · Jesse Dodge · Casey Fiesler · Pascale N Fung · Hanna Wallach -
2020 : Panel & Closing »
Tamara Broderick · Laurent Dinh · Neil Lawrence · Kristian Lum · Hanna Wallach · Sinead Williamson -
2020 : Q & A and Panel Session with Tom Mitchell, Jenn Wortman Vaughan, Sanjoy Dasgupta, and Finale Doshi-Velez »
Tom Mitchell · Jennifer Wortman Vaughan · Sanjoy Dasgupta · Finale Doshi-Velez · Zachary Lipton -
2020 : Morning keynote »
Hanna Wallach · Rosie Campbell -
2020 Workshop: I Can’t Believe It’s Not Better! Bridging the gap between theory and empiricism in probabilistic machine learning »
Jessica Forde · Francisco Ruiz · Melanie Fernandez Pradier · Aaron Schein · Finale Doshi-Velez · Isabel Valera · David Blei · Hanna Wallach -
2019 Poster: Poisson-Randomized Gamma Dynamical Systems »
Aaron Schein · Scott Linderman · Mingyuan Zhou · David Blei · Hanna Wallach -
2018 : Research Panel »
Sinead Williamson · Barbara Engelhardt · Tom Griffiths · Neil Lawrence · Hanna Wallach -
2018 : Panel on research process »
Zachary Lipton · Charles Sutton · Finale Doshi-Velez · Hanna Wallach · Suchi Saria · Rich Caruana · Thomas Rainforth -
2018 : Hanna Wallach - Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? »
Hanna Wallach -
2017 : The Unfair Externalities of Exploration »
Aleksandrs Slivkins · Jennifer Wortman Vaughan -
2017 : Poster spotlights »
Hiroshi Kuwajima · Masayuki Tanaka · Qingkai Liang · Matthieu Komorowski · Fanyu Que · Thalita F Drumond · Aniruddh Raghu · Leo Anthony Celi · Christina Göpfert · Andrew Ross · Sarah Tan · Rich Caruana · Yin Lou · Devinder Kumar · Graham Taylor · Forough Poursabzi-Sangdeh · Jennifer Wortman Vaughan · Hanna Wallach -
2017 Workshop: Learning in the Presence of Strategic Behavior »
Nika Haghtalab · Yishay Mansour · Tim Roughgarden · Vasilis Syrgkanis · Jennifer Wortman Vaughan -
2017 Poster: A Decomposition of Forecast Error in Prediction Markets »
Miro Dudik · Sebastien Lahaie · Ryan Rogers · Jennifer Wortman Vaughan -
2016 : Jennifer Wortman Vaughan: "The Communication Network Within the Crowd" »
Jennifer Wortman Vaughan -
2016 Poster: Poisson-Gamma dynamical systems »
Aaron Schein · Hanna Wallach · Mingyuan Zhou -
2016 Oral: Poisson-Gamma dynamical systems »
Aaron Schein · Hanna Wallach · Mingyuan Zhou -
2016 Poster: Flexible Models for Microclustering with Application to Entity Resolution »
Brenda Betancourt · Giacomo Zanella · Jeffrey Miller · Hanna Wallach · Abbas Zaidi · Beka Steorts -
2016 Tutorial: Crowdsourcing: Beyond Label Generation »
Jennifer Wortman Vaughan -
2015 Workshop: Bayesian Nonparametrics: The Next Generation »
Tamara Broderick · Nick Foti · Aaron Schein · Alex Tank · Hanna Wallach · Sinead Williamson -
2014 Workshop: NIPS’14 Workshop on Crowdsourcing and Machine Learning »
David Parkes · Denny Zhou · Chien-Ju Ho · Nihar Bhadresh Shah · Adish Singla · Jared Heyman · Edwin Simpson · Andreas Krause · Rafael Frongillo · Jennifer Wortman Vaughan · Panagiotis Papadimitriou · Damien Peters -
2014 Workshop: NIPS Workshop on Transactional Machine Learning and E-Commerce »
David Parkes · David H Wolpert · Jennifer Wortman Vaughan · Jacob D Abernethy · Amos Storkey · Mark Reid · Ping Jin · Nihar Bhadresh Shah · Mehryar Mohri · Luis E Ortiz · Robin Hanson · Aaron Roth · Satyen Kale · Sebastien Lahaie -
2014 Session: Oral Session 9 »
Jennifer Wortman Vaughan -
2013 Workshop: Topic Models: Computation, Application, and Evaluation »
David Mimno · Amr Ahmed · Jordan Boyd-Graber · Ankur Moitra · Hanna Wallach · Alexander Smola · David Blei · Anima Anandkumar -
2013 Workshop: Crowdsourcing: Theory, Algorithms and Applications »
Jennifer Wortman Vaughan · Greg Stoddard · Chien-Ju Ho · Adish Singla · Michael Bernstein · Devavrat Shah · Arpita Ghosh · Evgeniy Gabrilovich · Denny Zhou · Nikhil Devanur · Xi Chen · Alexander Ihler · Qiang Liu · Genevieve Patterson · Ashwinkumar Badanidiyuru Varadaraja · Hossein Azari Soufiani · Jacob Whitehill -
2012 Poster: Topic-Partitioned Multinetwork Embeddings »
Peter Krafft · Juston S Moore · Hanna Wallach · Bruce Desmarais -
2011 Workshop: Relations between machine learning problems - an approach to unify the field »
Robert Williamson · John Langford · Ulrike von Luxburg · Mark Reid · Jennifer Wortman Vaughan -
2010 Workshop: Computational Social Science and the Wisdom of Crowds »
Jennifer Wortman Vaughan · Hanna Wallach -
2009 Workshop: Applications for Topic Models: Text and Beyond »
David Blei · Jordan Boyd-Graber · Jonathan Chang · Katherine Heller · Hanna Wallach -
2009 Poster: Rethinking LDA: Why Priors Matter »
Hanna Wallach · David Mimno · Andrew McCallum -
2009 Spotlight: Rethinking LDA: Why Priors Matter »
Hanna Wallach · David Mimno · Andrew McCallum -
2007 Spotlight: Privacy-Preserving Belief Propagation and Sampling »
Michael Kearns · Jinsong Tan · Jennifer Wortman Vaughan -
2007 Poster: Privacy-Preserving Belief Propagation and Sampling »
Michael Kearns · Jinsong Tan · Jennifer Wortman Vaughan -
2007 Poster: Learning Bounds for Domain Adaptation »
John Blitzer · Yacov Crammer · Alex Kulesza · Fernando Pereira · Jennifer Wortman Vaughan -
2006 Poster: Learning from Multiple Sources »
Yacov Crammer · Michael Kearns · Jennifer Wortman Vaughan