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* : Indicates authors contributed equally.


2021

An Action-Oriented AI Policy Toolkit for Technology Audits by Community Advocates and Activists.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT*).

  Toolkit (Web Version, Digital PDF, Print PDF)

A Call for Scholar-Activism: A Response to Power and Technology.
ACM Interactions.


2020

Abdullah Almaatouq, Alejandro Noriega-Campero, Abdulrahman Alotaibi, P. M. Krafft, Mehdi Moussaid, Alex Pentland. (2020). Adaptive social networks promote the wisdom of crowds. Proceedings of the National Academy of Sciences of the United States of America (PNAS).

Paper

P. M. Krafft, Joan Donovan. (2020). Disinformation by Design: The Use of Evidence Collages and Platform Filtering in a Media Manipulation Campaign. Political Communication.

Paper

Emily Porter, P. M. Krafft, Brian Keegan. (2020). Visual Narratives and Collective Memory across Peer-Produced Accounts of Contested Sociopolitical Events. ACM Transactions on Social Computing.

Paper

P. M. Krafft, Meg Young, Michael Katell, Karen Huang, Ghislain Bugingo. (2020). Defining AI in Policy versus Practice. Proceedings of the 2020 AAAI/ACM Conference on AI, Ethics, and Society (AIES).

Paper

Michael Katell*, Meg Young*, Bernease Herman, Dharma Dailey, Vivian Guetler, Aaron Tam, Corinne Bintz, Daniella Raz, P. M. Krafft. (2020). Toward Situated Interventions for Algorithmic Equity: Lessons from the Field. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT*).

Paper


2019

Meg Young, Michael Katell, P. M. Krafft. (2019). Municipal Surveillance Regulation and Algorithmic Accountability. Big Data & Society. [2019 iConference Best Poster]

Paper

Seth Frey*, P. M. Krafft*, Brian Keegan*. (2019). “This Place Does What It Was Built For”: Designing Digital Institutions for Participatory Change. The 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW). [Best Paper Honorable Mention]

Paper

P. M. Krafft, Emma Spiro. (2019). Keeping Rumors in Proportion: Managing Uncertainty in Rumor Systems. ACM CHI Conference on Human Factors in Computing Systems (CHI).

Paper, Data

Yea-Seul Kim, Logan Walls, P. M. Krafft, Jessica Hullman. (2019). A Bayesian Cognition Approach to Improve Data Visualization. ACM CHI Conference on Human Factors in Computing Systems (CHI).

Paper

Abdullah Almaatouq, P. M. Krafft, Yarrow Dunham, David Rand, Alex Pentland. (2019). Turkers of the World Unite: Multilevel In-Group Bias Amongst Crowdworkers on Amazon Mechanical Turk. Social Psychological and Personality Science.

Paper, Data

P. M. Krafft. (2019). A Simple Computational Theory of General Collective Intelligence. Topics in Cognitive Science (topiCS).

Paper


2018

Eaman Jahani, P. M. Krafft, Yoshihiko Suhara, Esteban Moro Egido, Sandy Pentland. (2018). ScamCoins, S*** Posters, and the Search for the Next Bitcoin™: Collective Sensemaking in Cryptocurrency Discussions. The 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW).

Paper, Code/Data

P. M. Krafft, Tom Griffiths. (2018). Levels of Analysis in Computational Social Science. Annual Conference of the Cognitive Science Society (CogSci).

Paper

P. M. Krafft*, Nicolás Della Penna*, Alex Pentland. (2018). An Experimental Study of Cryptocurrency Market Dynamics. ACM CHI Conference on Human Factors in Computing Systems (CHI).

Paper, Code/Data

Dan Fu, Emily Wang, P. M. Krafft, Barbara Grosz. (2018). Influencing Flock Formation in Low-Density Settings. International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Paper, Code


2017

P. M. Krafft. (2017). A Rational Choice Framework for Collective Behavior. PhD Dissertation. Massachusetts Institute of Technology.

Manuscript

Elisa Celis, P. M. Krafft, Nisheeth Vishnoi. (2017). A Distributed Learning Dynamics in Social Groups. ACM Symposium on Principles of Distributed Computing (PODC).

Paper

P. M. Krafft, Michael Macy, Alex Pentland. (2017). Bots as Virtual Confederates: Design and Ethics. The 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW).

Paper

P. M. Krafft, Kaitlyn Zhou, Isabelle Edwards, Kate Starbird, Emma Spiro. (2017). Centralized, Parallel, and Distributed Information Processing during Collective Sensemaking. ACM CHI Conference on Human Factors in Computing Systems (CHI).

Paper, Code


2016

P. M. Krafft, Chris Baker, Alex Pentland, and Joshua Tenenbaum. (2016). Modeling Human Ad Hoc Coordination. The Thirtieth AAAI Conference on Artificial Intelligence (AAAI).

Paper, Code/Data

P. M. Krafft, Julia Zheng, Wei Pan, Nicolás Della Penna, Yaniv Altshuler, Erez Shmueli, Joshua B. Tenenbaum, and Alex Pentland. (2016). Human Collective Intelligence as Distributed Bayesian Inference. arXiv:1608.01987.

Paper

Elisa Celis, P. M. Krafft, and Nathan Kobe. (2016). Sequential Voting Promotes Collective Discovery in Social Recommendation Systems. The Tenth International AAAI Conference on Web and Social Media (ICWSM).

Paper, Code/Data

Ali Nahm, Alex Pentland, P. M. Krafft. (2016). Inferring Population Preferences via Mixtures of Spatial Voting Models. The 8th International Conference on Social Informatics (SocInfo).

Paper, Code/Data


2015

P. M. Krafft, Robert Hawkins, Alex Pentland, Noah Goodman, and Joshua Tenenbaum. (2015). Emergent Collective Sensing in Human Groups. Annual Conference of the Cognitive Science Society (CogSci). [Computational Modeling Prize for Best Paper on Applied Cognition]

Paper


2012

P. M. Krafft, Juston Moore, Bruce Desmarais, and Hanna Wallach. (2012). Topic-Partitioned Multinetwork Embeddings. Neural Information Processing Systems (NeurIPS).

Paper, Code, Data


Older Work

Chang Wang, P. M. Krafft, and Sridhar Mahadevan. (2011). Manifold Alignment. Appearing in Manifold Learning: Theory and Applications. Taylor and Francis CRC Press.

Manuscript

Evan Ray, P. M. Krafft, Patty Freedson, and John Staudenmayer. (2011). Novel Analytic Methods to Estimate Physical Activity from Accelerometer Data: An Opensource Web-based Tool. International Conference on Ambulatory Monitoring of Physical Activity and Movement. [Runner-up Best Student Presented Poster].

Abstract, Poster

P. M. Krafft and Sridhar Mahadevan. (2010). Feature-Preserving Embeddings for Topic Transfer. NeurIPS Workshop on Transfer Learning Via Rich Generative Models.

Extended Abstract

P. M. Krafft. (2010). Applying Deep Belief Networks to the Game of Go. Undergraduate Thesis.

Manuscript

P. M. Krafft and Michael Lavine. (2009). Modeling Light in Harvard Forest. The 15th Annual Massachusetts Statewide Undergraduate Research Conference.

Abstract, Presentation