Cambridge Forum on AI: Culture and Society publishes research both about and produced with artificial intelligence (AI): research about the social and cultural implications of AI, studies employing AI to develop new methodologies for critical research, and research oriented to new interdisciplinary paradigms for AI. In so doing, the journal brings together social science, humanities and artistic (SSHA) research on epistemologies, histories and practices of AI with computer science and data science (STEM) AI research, casting light on how AI applications translate, undermine or advance the diversity of social and cultural values and lifeworlds.

Cambridge Forum on AI: Culture and Society is part of the Cambridge Forum journal series, which progresses cross-disciplinary conversations on issues of global importance. 

The journal invites submissions for the Themed Issue: AI & Archives, guest edited by Katie Mackinnon (University of Copenhagen), Louis Ravn (University of Amsterdam), Nanna Bonde Thylstrup (University of Copenhagen), Eun Seo Jo (Cornell University) and Caroline Bassett (Cambridge University).  

In the first instance, please submit an abstract of 500 words (excluding references) to the journal at cfc@cambridge-org.demo.remotlog.com and cc the editors (Katie MacKinnon: kam@hum.ku.dk, Louis Ravn: lora@hum.ku.dk, Nanna Bonde Thylstrup: nannab@hum.ku.dk, Eun Seo Jo: unsojo@cornell.edu and Caroline Bassett: cab238@cam.ac.uk). If your abstract is accepted, you will be invited to submit a full paper. 

The deadline for submissions of abstracts is 1 November 2024.

The deadline for submissions of full papers is March 1 2025. Submissions of full papers should be made through the journal's online peer review system. Authors should consult the journal’s author instructions prior to submission.

All full papers will be peer reviewed in line with the journal’s review process. Acceptance of an abstract does not guarantee acceptance of the full paper.

Description

This themed issue examines the connections—past, present, and future—between artificial intelligence (AI), machine learning and archives. Our exploration is driven by three interrelated trends, which together indicate what we describe as an “archival turn” in AI research (Taurino & Smith, 2022) across the disciplinary spectrum from computational sciences to the social sciences and humanities.

Firstly, archival concepts such as provenance, traceability, appraisal and afterlives are increasingly mobilized to illuminate and confront issues in AI surrounding fairness, accountability, transparency, and ethics. Moreover, a growing number of AI and ML scholars are engaging with cultural theories of the archive as well as the normative orientation towards (data) justice in critical archival studies to better understand and address the ethical and political challenges posed by emerging machine learning systems (Jo & Gebru; Thylstrup, 2022; Kale et al., 2023; Basman, 2022).

Secondly, archival institutions themselves are increasingly incorporating AI into their professional workflows, prompting new discussions about human-machine interactions, ethical dilemmas, and methodological and critical potentials. Previous research has highlighted the growing use of AI in various aspects of recordkeeping, from curation to retrieval and annotation. As Colavizza et al. (2021) observe, “archives acquire AI capacities to organize their workflows around the big data they possess, as well as to offer their big data to outside organizations.” This integration of AI into archival practices raises critical questions about bias, representation, and the preservation of authenticity in our digital heritage, while also opening up new possibilities for reimagining the afterlives of archival data. Moreover, as archives evolve from collections of administrative records to repositories of data, new political dynamics emerge, reshaping the roles and responsibilities of institutions that generate and manage open data collections. These dynamics also raise classic questions of archival power: who is responsible and accountable for curation when AI is part of the work flow? What kinds of infrastructural architecture does AI bring to archives – how closed or open? And how could AI be employed to render the archive open, plural, agonistic and contestable?

Finally, the advent of AI technologies also challenges and redefines cultural archival theories (Thylstrup et al., 2021). What—or when (Acker, 2020)—is a record in AI regimes? How does cloud capitalism change structures of “archontic power” (Derrida, 1996; Harris, 2017)? How might we conceptualize AI regimes as unique archival formations within broader archaeologies of knowledge? And how do gendered and colonial archival structures of power endure within AI systems? These questions are explored not only in classic academic knowledge scholarship, but also – and increasingly – in aesthetic and artistic engagements with AI (Onuoha, 2022; Hunger, 2022; Salvaggio, 2024; Foldes, 2024).

We invite contributions that are empirical, theoretical, or methodological in nature, as well as commentaries from artists, professionals and policymakers, to further explore these critical, methodological, and conceptual intersections between AI and archives. We welcome contributions from all career levels and particularly encourage submissions from emerging career scholars and Majority World perspectives.

We invite contributions focusing on, but not restricted to, the following themes:

• Politics of open-source data archives (non-profits, community, activist, etc.)
• Data collection practices, networks, platforms, infrastructures and systems
• Archival aesthetics of/in AI
• Histories of AI and srchives
• STS perspectives on archives and AI
• Critical Data, Dataset and AI Studies of archival practices and infrastructures
• Data formats, quality, scope, scale
• Automated archival methodologies
• AI, archives and Digital Humanities
• Artistic engagements with AI and archives
• Exploration of the potentials and challenges of mobilizing concepts such as archive, provenance, appraisal in machine learning contexts
• Critical archival theories (including feminist, critical race, queer and post/decolonial theories) and AI 

In the first instance, please submit an abstract of 500 words (excluding references) to the journal at cfc@cambridge-org.demo.remotlog.com and cc the editors (Katie MacKinnon: kam@hum.ku.dk, Louis Ravn: lora@hum.ku.dk, Nanna Bonde Thylstrup: nannab@hum.ku.dk, Eun Seo Jo: unsojo@cornell.edu and Caroline Bassett: cab238@cam.ac.uk). If your abstract is accepted, you will be invited to submit a full paper. You will be notified within 2 weeks of the abstract submission deadline. 

The deadline for submissions of abstracts is Nov 1 2024.

The deadline for submissions of full papers is March 1 2025. Submissions of full papers should be made through the journal's online peer review system. Authors should consult the journal’s author instructions prior to submission.

All full papers will be peer reviewed in line with the journal’s review process. Acceptance of an abstract does not guarantee acceptance of the full paper.

Submission guidelines 

Cambridge Forum on AI: Culture and Society seeks to engage multiple subject disciplines and promote dialogue between policymakers and practitioners as well as academics. The journal therefore encourages authors to use an accessible writing style.

Authors have the option to submit a range of article types to the journal. Please see the journal’s author instructions for more information. 

Articles will be peer reviewed for both content and style. Articles will appear digitally and open access in the journal. 

All submissions should be made through the journal’s online peer review system. Author should consult the journal’s author instructions prior to submission.

All authors will be required to declare any funding and/or competing interests upon submission. See the journal’s Publishing Ethics guidelines for more information. 

Contacts

Questions regarding submission and peer review can be sent to the journal’s inbox at cfc@cambridge-org.demo.remotlog.com