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Human experience is the starting point for critical and creative practices of making across the College of Arts, Media and Design: making the built environment, designing expressive artifacts, sharing performances, or storytelling through news and media, for example.
These practices are being reshaped by advances in data-driven approaches and AI technology, and CAMD faculty bring a range of perspectives and expertise to that reshaping. In particular, research and teaching in CAMD advances understanding of:
Read here about some of the courses, research and initiatives CAMD faculty are leading in AI.
In art installations like “Boogaloo Bias,” professors Jennifer Gradecki and Derek Curry reverse-engineer AI decision-making systems to reveal how they work, “Because everyone has the right to know what these technologies promise and what they do —and don’t— deliver.”
The AI-Media Strategies Lab – AIMS Lab – focuses on the use of AI technologies in media industries, providing evidence-based recommendations to organizations, and producing research leveraging methods such as surveys, focus groups, experiments, and data analysis.
In this project, we build upon previous works such as Charticulator (Microsoft) and browser-based research studies to develop a two-part visual engine. The first engine generates a data stream that maps out aggregated, ecosystem-level behavior from the dataset. This stream is then used by the second visual engine to model individual behavior and introduce stochastic properties inherent in real-world scenarios.
In 2023-24, Laura Forlano (A+D/Comm), Rahul Bhargava (Journalism/A+D), Sylke Meyer (Theater/A+D), Joseph Reagle (Comm), Humbi Song (Architecture), and Victor Zappi (Music) curated resources and led a series of workshops in which faculty shared approaches, reflected, and experimented with generative AI for their teaching, research, creative practice, and professional fields. Topics included testing the robotproofness of assignments, the utilities and limits of AI tools in creative work, implications for expressive and commercial media like photography, and balancing deep ethical concerns with exploring creative applications. Additional speakers included Meg Heckman (journalism) on machine vision tools for journalism research, David Tames (A+D/media and screen studies) on AI research tools in the use case of virtual reality research, Grisha Coleman (A+D/theater) on data practices and resisting AI, and Tim Oillette (media and screen studies) on AI tools that benefit film production workflow. CAMD Faculty can continue to access resources at the Canvas site.
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Senior Associate Dean for Research and Partnerships, Professor
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Rahul Bhargava, Amanda Brea, Victoria Palacin, Laura Perovich and Jesse Hinson, “Data Theatre as an Entry Point to Data Literacy” Educational Technology & Society, Vol. 25, No. 4 (October 2022) https://www.jstor.org/stable/48695984
Rahul Bhargava, Community Data: Creative Approaches to Empowering People with Information (Oxford University Press 2024) https://global.oup.com/academic/product/community-data-9780198911630
S. Lee, Myo Chung, N. Kim, S.M. Jones-Jang, N. Krämer, & R. McEwen “Public Perceptions of ChatGPT: Exploring How Nonexperts Evaluate Its Risks and Benefits” Technology, Mind, and Behavior, 5(4: Winter 2024) https://tmb.apaopen.org/pub/ki45ziga/release/1
Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini, “Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets” https://ieeexplore.ieee.org/abstract/document/9937064
S. Lenzi, G. Terenghi, D. Meacci, A. Moreno Fernandez-de-Leceta, Paolo Ciuccarelli, “The Design of Datascapes: Toward a Design Framework for Sonification for Anomaly Detection in AI-supported Networked Environments” https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2023.1254678/full
Tucker J. Marion, Mohsen Moghaddam, Paolo Ciuccarelli and Lu Wang, “AI for User-Centered New Product Development—from Large-Scale Need Elicitation to Generative Design” https://www.oreilly.com/library/view/the-pdma-handbook/9781119890218/c22.xhtml
Grisha Coleman, Brenda McCaffrey, “The Movement Undercommons: Movement Analysis as Meaning Making in a Time of Global Migrations” https://isea-archives.siggraph.org/wp-content/uploads/2021/02/2018_Coleman_etal_The_Movement_Undercommons.pdf
Derek Curry, “Artistic Defamiliarization in the Age of Algorithmic Prediction” https://direct.mit.edu/leon/article-abstract/56/2/177/113466/Artistic-Defamiliarization-in-the-Age-of
Smit Desai, Michal Twidale, “Using Playful Metaphors to Conceptualize Practical Use of ChatGPT: An Autoethnography” https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/pra2.816
Demi Fang, Sophia V. Kuhn, Walter Kaufmann, Michael A. Kraus, Caitlin Mueller, “Quantifying the influence of continuous and discrete design decisions using sensitivities,” https://doi.org/10.1515/9783111162683-031
Laura Forlano and Itziar Barrio, “From Data Doubles to Data Demons” https://muse.jhu.edu/article/926028
Nabeel Gillani, Rebecca Eynon, Catherine Chiabaut and Kelsey Finkel, “Unpacking the ‘Black Box’ of AI in Education” https://www.j-ets.net/collection/published-issues/26_1
Jennifer Gradecki, Derek Curry, “Crowd-Sourced Intelligence Agency: Prototyping Counterveillance,” https://journals.sagepub.com/doi/full/10.1177/2053951717693259
Jennifer Gradecki, “The Critical Counterpoints of Dataveillance Artists” https://direct.mit.edu/leon/article-abstract/56/2/164/113458/The-Critical-Counterpoints-of-Dataveillance
Chenyan Jia, Martin J. Riedl & Samuel Wolley, “Promises and Perils of Automated Journalism” https://doi.org/10.1080/1461670X.2023.2289881
Adriana Alvarado Garcia, Christopher Le Dantec, “Quotidian Report: Grassroots Data Practices to Address Public Safety” https://dl.acm.org/doi/10.1145/3274286
Clifford Lee and Elisabeth Soep, Code for What? Computer Science for Storytelling and Social Justice https://mitpress.mit.edu/9780262047456/code-for-what/
Jun Liu and Chuncheng Liu, “The Politics of Governance by Quantification Infrastructure” https://journals.sagepub.com/doi/full/10.1177/08969205241298291
Deirdre Loughridge, Sounding Human: Music and Machines 1740/2020, “Coda: Learning Machines” https://press.uchicago.edu/ucp/books/book/chicago/S/bo208042715.html
Dietmar Offenhuber, “Shapes and Frictions of Synthetic Data” https://journals.sagepub.com/doi/10.1177/20539517241249390
Carlos Sandoval Olascoaga, “Painting with Data: Visually based open-source tool for geo-computing” https://journals.sagepub.com/doi/full/10.1177/23998083231193321
Travis Lloyd, Joseph Reagle, Mor Naaman, “’There Has to Be a Lot That We’re Missing’: Moderating AI-Generated Content on Reddit” https://arxiv.org/pdf/2311.12702
Mariel Pettee, Chase Shimmin, Douglas Duhamie, Ilya Vidrin, “Beyond Imitation: Generative and Variational Choreography via Machine Learning” https://computationalcreativity.net/iccc2019/papers/iccc19-paper-51.pdf
Zhouran Lu, Dakuo Wang, Ming Yin “Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making” https://dl-acm-org.ezproxy.neu.edu/doi/abs/10.1145/3653708
John Wihbey, “AI and Epistemic Risk for Democracy: A Coming Crisis of Public Knowledge?” https://ssrn.com/abstract=4805026
John Wihbey, Garrett Morrow, “Social Media’s New Referees?: Public Attitudes Toward AI Content Moderation Bots Across Three Countries” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4660772
Learn more about CAMD research and innovation