Alan Liu

“AI Virtue: What’s ‘Good’ Knowledge in the Age of Artificial Intelligence?,” Faculty Research Lecturer Award Talk, 9 February 2026.

• Faculty Research Lecturer Award: “Established in 1954, the Faculty Research Lecturer is the highest honor the UCSB faculty can bestow on one of its members. Each year the Academic Senate recognizes one individual for their outstanding academic and creative achievements and gives faculty, students, and the citizens of the UCSB community an opportunity to learn about their work. The Committee on Faculty Research Lecturer is pleased to present Dr. Alan Liu, Distinguished Professor of English, as our 2025 Faculty Research Lecturer.”
  • Alan’s Response to Being Named the UCSB Faculty Research Lecturer for 2024-25.
• Talk Abstract: In the age of AI, what will be good knowledge? Alan Liu applies digital humanities methods to map epistemic virtues (like “true,” “accurate,” “creative”) used to discuss artificial intelligence. “Creativity” comes in for special attention as an example. Exploring this landscape of value, he considers how a framework might be developed for evaluating the knowledge worth of AI—one less locked into values formed around pre-AI “knowledge work” agents or structures, and more open to the future values of “generativity.”

“AI Virtue: ‘Good’ Knowledge in the Age of Artificial Intelligence,” Texas Tech Comparative Literature Symposium on “AI and the Futures of the Human,” 10 April 2026.

• Abstract: In the age of AI, what will be good knowledge? Alan Liu applies digital humanities methods to map epistemic virtues (like “true,” “accurate,” “creative”) used to discuss artificial intelligence. “Creativity” comes in for special attention as an example. Exploring this landscape of value, he considers how a framework might be developed for evaluating the knowledge worth of AI—one less locked into values formed around pre-AI “knowledge work” agents or structures, and more open to the future values of “generativity.”

“On Humanities Communication,” presentation for advisory board of the Spence Wilson Center for Interdiscplinary Humanities, Rhodes College, 11 December 2025.

Citation: Alan Liu, “Humanities Definitions Research Project: An Experiment with Agentic AI,” 5 October 2025 (rev. 6 Oct. 1025), https://liu.english.ucsb.edu/humanities-definitions-research-project-an-experiment-with-agentic-ai/

Overview

This in-progress “Humanities Definitions Research Project” using the Fellou.ai agentic browser (home page |  “quick tour”) was conducted as an experiment with agentic AI. Fellou is an early leader in the field of “agentic AIs” for research tasks (reviews of Fellou: example 1 | example 2). My ultimate goal with this project is to assist the new Center for Humanities Communication, which I cofounded, in gathering definitions of, and statements about, the humanities that can facilitate the development of training workshops and resources for communicating what the humanities are and do. But my immediate goal is just to test how useful agentic AI can be at its present level of development for research tasks that are difficult for humans to accomplish efficiently, systematically, or in well-planned ways…. [continued]

Citation: “Liberal Arts Pantocrator: Addendum to ‘Data Science and the Post-Liberal Arts University.’” KCWorks, 24 Sept 2025. https://doi.org/10.17613/vnybw-f6a84.

• Abstract: The following fragment discussing two early pictorial illustrations of the liberal arts was originally part of my article on “Data Science and the Post-Liberal Arts University” (Critical Inquiry 51, no. 4 (2025): 597–618) but had to be cut due to length limitations. The fragment complements the article’s final section on “A Post-Liberal Arts Curriculum (2),” which discusses what is “needed for the post-liberal arts to enhance existing efforts in the liberal arts to be more inclusive of their social margins and global peripheries—in particular, by acknowledging the heritage of applied knowledge embedded in the very notion of such margins and peripheries” (612-618).

Citation: “Data Science and the Post-Liberal Arts University.” Critical Inquiry 51, no. 4 (2025): 597–618. https://doi.org/10.1086/735621.

• Abstract: Data science has grown explosively in higher education, offering undergraduate degrees in the US on a “core and domains” curricular model that overlaps with—and in an intriguing way—replicates the multidisciplinary model of liberal arts education. This essay treats data science as a pathfinder for the continuing evolution of American higher education from the liberal arts to a post-liberal arts centered on applied knowledge, including today’s new modes of predictive/generative knowledge. The essay builds toward a consideration of how the post-liberal arts university can teach students both to apply knowledge and to know the meaning (historical, intellectual, and social) of application. A key for the post-liberal arts university will be to teach preprofessionalism in ways that do not just acknowledge the historical exclusion of the people of applied knowledge (at the social margins and global peripheries) from liberal arts education for the “free man” but turns such acknowledgement into new forms of liberal arts knowledge. Data science, the essay concludes, has the potential to contribute novel ways of conceptualizing intersectionality in general and in relation to applied knowledge. But data science is also constrained in this regard on the global scene where data power belongs to many regions and actors without a shared tradition of liberal arts education and thus a framework for a post-liberal arts carrying on shared ideas and practices of freedom.

“AI Virtue: Generativity and Epistemic Value,” Santa Fe Institute workshop on “The New New Science,” Santa Fe, NM, 16 September 2025.

• Abstract: In the age of AI, what will be “good” knowledge? This talk maps epistemic virtues (like “true,” “accurate,” “creative”) used to discuss AI in a corpus of 553 journal articles on AI published in 2024. “Creativity” comes in for special attention as an example. The goal is to theorize how a more integrated framework might be developed for evaluating the worth of AI—one less locked into understandings of value adapted to pre-AI agents and structures of knowledge work. The talk is accompanied by a digital kit for exploring data models of discourse on AI.

Scholarship on Machine Learning/AI  and Close Reading culled by Scott Newstok from his Close Reading Archive

Scott Newstok shared this list with Alan Liu on May 18, 2025, with permission to share publicly.

• Joanna Beaufoy and Lars Kjær, “Can Automatic Reading Be Close?”
  https://researchprofiles.ku.dk/en/publications/can-automatic-reading-be-close-on-the-implications-of-using-a-lar
• Boston University, “AI and the Humanities”
  https://www.bu.edu/research/files/2025/04/AI-Slides-ROT-4_16.pdf
• Jenny Costello, Zeljka Doljanin, Naomi McAreavey, Fionnuala Walsh, “Teaching Humanities and Social Sciences in the Era of GenerativeAI”
  https://www.ucd.ie/artshumanities/t4media/Case%20Studies%20from%20around%20Ireland.pdf
• Martin Paul Eve, Close Reading with Computers Textual Scholarship, Computational Formalism, and David Mitchell’s Cloud Atlas (Stanford, 2019)
  https://library.oapen.org/handle/20.500.12657/36879
• George Fletcher, Olha Nahurna, Matvii Prytula, Julia Stoyanovich, “CREDAL: Close Reading of Data Models” https://arxiv.org/pdf/2502.07943
• Luciano Floridi, “Distant Writing: Literary Production in the Age of Artificial Intelligence” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5232088
• Ashley Gong, Katy Ilonka Gero, Mark Schiefsky, “Augmented Close Reading for Classical Latin using BERT for Intertextual Exploration,”
  https://aclanthology.org/2025.nlp4dh-1.35.pdf
• John Guillory, On Close Reading
  https://press.uchicago.edu/ucp/books/book/chicago/O/bo239363263.html
• Jason Gulya, “The future will desperately need Close Reading”
  https://www.linkedin.com/posts/jason-gulya_the-future-will-desperately-need-close-reading-activity-7222610042062024705-2tK3/
• Carl Hendrick, “Ultra-Processed Minds: The End of Deep Reading and What It Costs Us”
  https://carlhendrick.substack.com/p/ultra-processed-minds-the-end-of
• Andres Karjus, “Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence”  https://www.nature.com/articles/s41599-025-04503-w
• Mun-Hou Lo, “Close Reading and Its Alternatives”
  https://www.routledge.com/Close-Reading-and-Its-Alternatives-An-Essential-Reader/Lo/p/book/9781032775746
• Michael Lockett & Scott Jarvie, “A Close Writing Playbook: Coupling Creative Writing with Close Reading: An Editorial Introduction”
  https://www.tandfonline.com/doi/full/10.1080/1358684X.2024.2445175
• Warmhold Jan Thomas Mollema, “AI-generated literature, distant writing and the reader: Reflections on Floridi and Calvino” https://philpapers.org/archive/MOLALD-2.pdf
• Rod Naquin, “Close reading with NotebookLM” https://rodjnaquin.substack.com/p/close-reading-with-notebooklm
• Andrew Rejan, “Close Reading for the Twenty-First Century: Rehumanizing Literary Reading” https://publicationsncte.org/content/journals/10.58680/ej2024114259
• Ward Risvold, “Close Reading in the Age of AI: A Critical Digital Literacy” https://www.torontomu.ca/centre-for-excellence-in-learning-and-teaching/workshops-and-events/2025/02/close-reading-in-the-age-of-ai–a-critical-digital-literacy/
• Katia Schwerzmann, “Workshop: New Reading Scenes. On Machine Reading and Reading Machine Learning Research”
  https://nachrichten.idw-online.de/2025/02/12/workshop-new-reading-scenes-on-machine-reading-and-reading-machine-learning-research
• Yael Segalovitz, “How Close Reading Made Us: The Transnational Legacies of New Criticism”
  https://muse.jhu.edu/book/133918
• Dan Sinkyin, Johanna Winant, “Close Reading for the Twenty-First Century”
  https://press.princeton.edu/books/paperback/9780691265704/close-reading-for-the-twenty-first-century
• Peiqi Sui, Juan Diego Rodriguez, Philippe Laban, Dean Murphy, Joseph P. Dexter, Richard Jean So, Samuel Baker, Pramit Chaudhuri, “KRISTEVA: Close Reading as a Novel Task for Benchmarking Interpretive Reasoning” https://arxiv.org/abs/2505.09825 
• symplokē, special issue “The Ethics of Close Reading?”
  https://muse.jhu.edu/issue/53972
• Katherine Thai, Mohit Iyyer, “Literary Evidence Retrieval via Long-Context Language Models,” arXiv (June 3, 2025). https://arxiv.org/html/2506.03090v1
• UCL Graduate Conference 2024: “Close Reading in the Digital World”
  https://call-for-papers.sas.upenn.edu/cfp/2024/04/04/close-reading-in-the-digital-world
• Terry Underwood, “Distant Writing: Time Travel to the Distant Future” https://terryu.substack.com/p/distant-writing-time-travel-to-the
• Melanie Walsh, Anna Preus, Maria Antonia, “Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets” https://arxiv.org/abs/2406.18906

“Teaching Digital Humanities for the Post Liberal Arts University,” session 743 on “The (Im)Possibility of a DH Textbook.” MLA 2025, New Orleans, 12 January 2025.

• Session information
• Abstract: Liu suggests that a hypothetical DH textbook would need to be positioned in a larger ecosystem of readers, anthologies, and casebooks designed for today’s functionally post liberal art students hungry for applied modes of knowledge but also, in the spirit of the older liberal arts, yearning to know more expansively what “application” is actually for.

“For Margie – Seeker, Keeper,” ELO2024 conference, plenary panel on “Celebrating Marjorie C. Luesebrink” (Part 1), 19 July 2024.

• Abstract: Liu narrates how Marjorie Luesebrink’s themes and interests are braided together with his own research and teaching–specifically in his signature lecture course (English 25 at UC Santa Barbara) on the history of media and information. A later version of Liu’s course included the spreadsheet fiction that Margie was working on (Tin Towns and other Excel Fictions). The earlier version of the course from 2000-2007 concluded with lectures on Luesebrink’s major hypertext novel Califia, which captured so much of the history and themes of media and information (in their social and personal impact) that it was the perfect capstone for a course titled “The Culture of Information” ( and in later years “Literature and the Information, Media, & Communication Revolutions”).
• Video recording of panel (view online or download .mp4 video):
  • Lai-Tze Fan — Introduction to Panel (0:00 – 6:14)
  • Stephanie Strickland (6:14 – 14:03)
  • Alan Liu (14:15 – 30:42)
  • Stuart Moulthrop (30:42 – 44:17)
  • Discussion (44:17 – 1:02:27)
• Slides for my talk available at:
  • https://bit.ly/ForMargie_ELO2024
  • and https://zenodo.org/uploads/12785924 (DOI: 10.5281/zenodo.12785924)

“Reframing the Humanities as Useful,” Humanities for the Public Good Closing Symposium: A Celebration, Obermann Center, University of Iowa, 1 March 2024.

• Symposium Program

“Toward New MLA Guidelines on Evalutating Digital Scholarship,” session 404 on “Evaluating Digital Scholarship Today: Problems and Solutions” organized by the MLA Committee on Information Technology, MLA 2024, Philadelphia, 6 January 2024.

• Session information
• Abstract: As a respondent to talks at this panel, Alan Liu will share a preview of the MLA Committee on Information Technology’s work in drafting revised MLA guidelines for evaluating digital scholarlship. (The previous revision of the guidelines was issued in 2012.)

Citation: Alan Liu, “Messages and Values ??in the Age of Machine Learning: From Postcards to Social Media,” Prace Kulturoznawcze 26, no. 4 (2023): 125–29, https://doi.org/10.19195/0860-6668.26.4.8.

• Excerpt (first paragraphs):
  If Stanislaw Pietraszko were to update his essay “Messages and Values” today, would he write about social media instead of postcards?
  Superficially, the analogy between postcards and social media seems unavoidable. After all, Facebook, Twitter, Instagram, TikTok, and other social media transmit short messages of text, images, and/or videos characterized by the same feature that Pietraszko noticed distinguishes postcards from letters: “the public availability of the verbal text” (and other content). One can assume, then, that Pietraszko and other scholars of the “axiosemiotics” of the postcard such as Zdzislaw W?sik would today also wish to discuss social media forms whose “excessive” and “redundant” performativity (to use Pietraszko’s terms) make them objects not just of information but also, and often primarily, of culture. After all, the visual “filters” that users frequently apply to their Instagram or TikTok posts are perfect examples of such excess or redundancy. There is almost no informational and only cultural value, for instance, in making oneself look like a cat.
  Yet one doubts that Pietraszko would have been content with just a superficial comparison of postcards to social media. His theoretical analysis was systemic in its aims, focusing on postcards to formulate a general relation between “messages and values” based on the difference between the semiotic function of information and the axiological values of culture. One surmises that today Pietraszko would want to pursue the same kind of systemic analysis by looking deeper into the systems of information and culture behind social media—a level of analysis, however, that poses challenges to his axiosemiotic approach.
• Excerpt (last paragraph):
  “Culture … is neither communication nor information,” Pietraszko wished to believe. But for those working in data science now, the challenge is that communication and information are saturated by cultural values that cannot be partitioned off. Reciprocally, for those working in cultural studies, the challenge is that cultural values increasingly are fused to the instrumental functions of communication and information (as when a “like” in social media is exploited by the system to promote an advertisement). Instrumental functions cannot be compartmentalized from values because in the final analysis the very concept of instrumentality or functionality (and its underlying logics of cause and effect) are changing. Functionalism now incorporates probabilistic operations of predictive modeling that—as technology companies like to say—“just work,” but work in semiotically non-understandable ways that perhaps most resemble how culture works.

“Where Does Data Science Fit in a Liberal Arts Academy?”, seminar on “The Place of Data” (part of the Stanford CESTA/Mellon Sawyer Seminar series on “The Data that Divides Us: Recalibrating Data Methods for New Knowledge Frameworks Across the Humanities”), Stanford University, 2 November 2023.

• Seminar information
• Notes and Links for Stanford Literary Lab Visit

“Toward a Center for Humanities Communication,” session on “How to Communicate Your Humanities Scholarship to the Public,” National Humanities Conference, Indianapolis, 26 October 2023.

• Session information
• Abstract: Alan Liu initiates this panel created by organizers of the Center for Digital Humanities (CHC) initiative by introducing the concept and plan of the new initiative.

“Reframing the Humanities as Useful,” session on “From Thinking to Doing: Making the Humanities Public,” National Humanities Conference, Indianapolis, 26 October 2023.

• Session information
• Abstract: Alan Liu will draw on the findings of the Mellon Foundation funded “WhatEvery1Says” (WE1S) project he directed to suggest specific areas where innovative organizations, programs, structures, practices, and media are needed to design fresh ways of engaging the public with the humanities. These include ways to activate the material culture of the humanities (not just curated objects but the artifacts of citizens and local communities); create resources and practices for bridging between the “personal” humanities (e.g., a poem one loves) and community, state, national, and global humanities issues; experiment with new media forms to communicate the humanities; and draw on the experiences and heritage of underrepresented social groups.

“What is Good Writing in the Age of ChatGPT?”, speech for English Department commencement ceremony, 18 June 2023.

• Full Text of Speech
   
  What is good writing in the age of ChatGPT (which, as you know, is the most celebrated of the new generative artificial-intelligence tools based on large language models that can write prose, verse, and lies just like a human being)?

  That’s the question I call on you as graduating English majors to help society answer as you bring your skills in writing and speaking well — and in knowing well through language and its literatures — into the world….

  [go to full speech]

“What is Good Writing in the Age of ChatGPT?”, speech for English Department commencement ceremony, 18 June 2023.

• Full Text of Speech
   
  What is good writing in the age of ChatGPT (which, as you know, is the most celebrated of the new generative artificial-intelligence tools based on large language models that can write prose, verse, and lies just like a human being)?

  That’s the question I call on you as graduating English majors to help society answer as you bring your skills in writing and speaking well — and in knowing well through language and its literatures — into the world….

  [go to full speech published as blog post]

“Agrippa (A Book of the Dead) and the Sociology of New Media,” virtual presentation at the Agrippa (A Book of the Dead), Oxford U., 18 May 2023.

“Reframing the Use of the Humanities,” Panel on “Can the Humanities Be ‘Useful’” at the Symposium on 10th-Year Annersary of The Heart of the Matter Report, American Academy of Arts & Sciences, Cambridge, MA, 2 April 2023.

“Infrastructure as Epistemic Value in the Digital Humanities,” Symposium on “The Integrative Potential of Epistemic Virtues for the Digital Humanities,” German Institute for Japanese Studies, Tokyo, 27 January 2023.

• Abstract: In seeking legitimacy as a field of study, the digital humanities have cultivated epistemic values that combine some from the sciences (such as evidence, precision, and reproducibility) and some from the contemporary humanities (such as being “interpretative” and “critical”). These values sum up at a higher level in the more general epistemic values that the digital humanities have made it a priority to attain: being “meaningful” and “cultural critical.” (Some in the humanities have been skeptical that quantitative and other DH methods can be interpretatively meaningful or engage in sociopolitical and cultural critique.)
  But there is one other general epistemic value in the digital humanities that makes the field distinctive among the humanities: valuing the “infrastructural” (i.e., thinking about and developing infrastructure as an interpretative and critical object). This talk surveys some of the intellectual approaches that converge in current “critical infrastructure studies,” inquires into the constitutive epistemic values underlying such studies, and concludes with a suggestion about how textual analysis of the “verbs” as opposed to “nouns” of infrastructure can unlock the “black box” of these values.

“Thinking at the ‘Enterprise Technology Systems’ Level.” Panel on What Do We Want In A Research Platform Of The Future? (session 620), Modern Language Association convention, San Francisco, 7 January 2023.

“Research Learning: Digital Project Courses & Teaching Research Practices.” Panel on From Pedagogy to Research and Back Again (session 432), Modern Language Association convention, San Francisco, 7 January 2023.

Citation: “Theses on Large Language Models and ‘Good’ Writing” Alan Liu, 4 December 2022. doi:

4 December 2022

1/8 As an English professor working in the digital humanities, my takeaway from ChatGPT (& large-language-model discourse generators in general) is that society will soon need to decide which values associated with “good” writing can and will be offloaded to LLMs so that the value added by humans can be shifted to a smaller or restructured spectrum of the functions of “good” writing for which humans can be recognized, rewarded, and held responsible.

“WhatEvery1Says: The Humanities in Public Discourse.” Humanities & Fine Arts Digital Humanities Showcase, UC Santa Barbara, 18 November 2022.

• Slides for lecture: bit.ly/Liu_11_18_2022

“WhatEvery1Says: The Humanities in Public Discourse.” Panel on “The Public, the Humanities, and the Public Humanities” at the National Humanities Conference 2022, Los Angeles, 12 November 2022.

• Slides for lecture: bit.ly/Liu_NHC2022

“Research-based Humanities Advocacy: 4Humanities.org and the WhatEvery1Says Project” Talk for 25Humans For the Humanities, hosted virtually by Goethe University, Frankfurt, 14 December 2021.

“Where Does Data Science Fit in a Liberal Arts University?” Data Science Summit, UCSB, 3 December 2021.

• Video recording of this talk (15.39 min.)

“What Everyone Says About the Humanities: The Challenge Posed by the Public Perception of the Humanities in the Media.” Daedalus authors’ meeting for contributors to special issue on “The Humanities in American Life,” 10 Sept. 2021 (conducted over Zoom).

• Slides for talk.

“Digital Humanities and Critical Infrastructure Studies — An Overview.” King’s College, London, 21 June 2021, 5:10-5:50 pm London time. Keynote lecture for the “Infrastructural Interventions” workshop in the Digital Humanities & Critical Infrastructure Studies series organized by Urszula Pawlicka-Deger. (Delivered by Microsoft Teams meeting.)

• Abstract: In this talk, Alan Liu provides an introduction to “critical infrastructure studies” and the place of the digital humanities in it. What have been the main approaches to infrastructure that today make the topic of such compelling socio-political, technological, media-informatic, cultural, historical, and artistic interest across the disciplines? How are the digital humanities positioned in relation to those approaches; and what is “critical” about that relation?
• Useful links for citations and other material mentioned in the talk:
  • CIstudies.org Bibliography: bit.ly/cistudies_biblio
  • “CI Studies Primer”: bit.ly/cistudies_primer
  • Syllabus for Alan Liu’s 2020 graduate seminar on “Critical Infrastructure Studies”: bit.ly/cistudies_course2020

“WhatEvery1Says: Data Mining Media Coverage of the Humanities.” The Education University of Hong Kong, 8 April 2021.

• Abstract: Backed by a three-year, $1.1 million grant from the Andrew W. Mellon Foundation, the WhatEvery1Says (WE1S) project uses digital humanities methods—primarily topic modeling, complemented by such other methods as text classification—to study media discourse about the humanities at big data scales. Alan Liu, director of WE1S, will give an overview of the project and its open-source datasets and topic-model analysis, visualization, and interpretation tools (as well as its surveys of students and others providing a ground-truth perspective on views on the humanities). He will also highlight selected project outputs, including explanations of findings, methods, data, and tools in a “card” format inspired by new practices in data-model reporting.
  The goal of the WE1S project is to provide advocates for the humanities with research-based materials and strategies for effective communication about the value of humanistic study and knowledge in today’s world.

“WhatEvery1Says: Data Mining Media Coverage of the Humanities.” Digital Tools for Interdisciplinary Humanities Research Workshop Series, Public Humanities Design Studio, University of California, Merced, 15 March 2021.

• Abstract: Backed by a three-year, $1.1 million grant from the Andrew W. Mellon Foundation, the WhatEvery1Says (WE1S) project uses digital humanities methods—primarily topic modeling, complemented by such other methods as text classification—to study media discourse about the humanities at big data scales. Alan Liu, director of WE1S, will give an overview of the project and its open-source datasets and topic-model analysis, visualization, and interpretation tools (as well as its surveys of students and others providing a ground-truth perspective on views on the humanities). He will also highlight selected project outputs, including explanations of findings, methods, data, and tools in a “card” format inspired by new practices in data-model reporting.
  The goal of the WE1S project is to provide advocates for the humanities with research-based materials and strategies for effective communication about the value of humanistic study and knowledge in today’s world.

“Writing Data: Literary Scholars and New Forms of Public Writing.” Panel on “Public Humanities in the Age of Precarity, Modern Language Association convention (virtually presented panel), 7 January 2021.

“Critical Infrastructure Studies — A Primer.” Furman University, 12 November 2020, 1:30-2:30 pm Pacific time (4:30-5:30 Eastern time). (Lecture delivered by Zoom webinar: registration.)

• Abstract: What have been the main approaches to the study of infrastructure that now combine to make the topic of such compelling socio-political, technological, media-informatic, cultural, historical, and artistic interest across the disciplines? In this talk, Alan Liu provides an introduction to “critical infrastructure studies,” focusing on why multidisciplinary perspectives–sometimes tensely divergent in their premises even when converging to make, for example, a “bridge” or a “barrier”–are needed to imagine good infrastructure.
• Useful links for citations and other material mentioned in the talk:
  • CIstudies.org Bibliography: bit.ly/cistudies_biblio
  • “CI Studies Primer”: bit.ly/cistudies_primer
  • Syllabus for Alan Liu’s 2020 graduate seminar on “Critical Infrastructure Studies”: bit.ly/cistudies_course2020

“Friending the Past: The Sense of History in the Digital Age — A Virtual Talk.” History Department, U. California, Santa Barbara (4 May 2020, 11:00 AM-12:00 PM, Pacific Daylight Time) (Zoom meeting information sent after request through this form.)

• Abstract: Can today’s society, increasingly captivated by a constant flow of information, share a sense of history? How did our media-making forebears balance the tension between the present and the absent, the individual and the collective, the static and the dynamic—and how do our current digital networks disrupt these same balances? Can our social media, with its fleeting nature, even be considered social at all? In Friending the Past, Alan Liu proposes fresh answers to these innovative questions of connection. He explores how we can learn from the relationship between past societies whose media forms fostered a communal and self-aware sense of history. Interlaced among these inquiries, Liu shows how extensive ‘network archaeologies’ can be constructed as novel ways of thinking about our affiliations with time and with each other.
• Video recording of this talk (47 min.)

Citation:”Data Moves: Libraries and Data Science Workflows.” Libraries and Archives in the Digital Age. Ed. Susan Mizruchi. Cham: Palgrave Macmillan, 2020: 211-219.

• Abstract: Library-based collections and repositories are today advancing well beyond accumulating resources in digital form for the purposes of searching, reading, and other primary access. New advances toward treating collections as “always already data” facilitate next-generation computational uses of digitized materials—for example, treating collections as datasets for advanced datamining analysis.
          In considering how library collections can serve as data for a variety of data ingestion, transformation, analysis, reproduction, presentation, and circulation purposes, it may be useful to compare examples of data workflows across disciplines to identify common data-analysis “moves” as well as points in the data trajectory that are especially in need of library support because they are for a variety of reasons brittle. Drawing on the precedent of so-called in silico science—which has had a ten-year start on developing methods and standards for tracking the provenance of data, annotating and visualizing data analysis workflows for reproducibility, and comparing data workflows in different fields—Liu argues that other disciplines such as the humanities and social sciences can exploit today’s library data collections in similar ways. The goal is twofold: first, open, shareable, and reproducible data scholarship, and second, higher or meta-level analysis of such scholarship. For example, might methods for comparing data workflows in the sciences (seeing, e.g., how astrophysics compares with medical science in using data) be extended across the disciplines to the digital humanities, digital arts, and digital social sciences? Beyond borrowing science data paradigms for other disciplines, Liu also thinks in the reverse direction. He draws on the twentieth-century tradition of literary and ethnographical analysis—for example, the idea of the narrative “motif” or “move” (in the Russian: mov)—to suggest that humanities and social science approaches to data workflows are just as crucial as scientific ones. After all, however one analyzes data (and in which field), one ultimately has to tell the story of that workflow and its results. That puts the problem squarely in the domain of narrative motifs and moves, which Liu argues can be matched to data workflow moves.

“Humans in the Loop: Humanities Hermeneutics and Machine Learning.” Keynote for DHd2020 (7th Annual Conference of the German Society for Digital Humanities), University of Paderborn, 6 March 2020.

• Abstract: As indicated by the emergent research fields of computational “interpretability” and “explainability,” machine learning creates fundamental hermeneutical problems. One of the least understood aspects of machine learning is how humans learn from machine learning. How does an individual, team, organization, or society “read” computational “distant reading” when it is performed by complex algorithms on immense datasets? Can methods of interpretation familiar to the humanities (e.g., traditional or poststructuralist ways of relating the general and the specific, the abstract and the concrete, the structure and the event, or the same and the different) be applied to machine learning? Further, can such traditions be applied with the explicitness, standardization, and reproducibility needed to engage meaningfully with the different Spielräum – scope for “play” (as in the “play of a rope,” “wiggle room,” or machine-part “tolerance”) – of computation? If so, how might that change the hermeneutics of the humanities themselves?
  In his keynote lecture, Alan Liu uses the example of the formalized “interpretation protocol” for topic models he is developing for the Mellon Foundation funded WhatEvery1Says project (which is text-analyzing millions of newspaper articles mentioning the humanities) to reflect on how humanistic traditions of interpretation can contribute to machine learning. But he also suggests how machine learning changes humanistic interpretation through fresh ideas about wholes and parts, mimetic representation and probabilistic modeling, and similarity and difference (or identity and culture).
• Video of lecture

“The WhatEvery1Says (WE1S) Project.” Mellon Research Forum Convening, University of California, Irvine, January 31, 2020.