Humanist Discussion Group, Vol. 38, No. 361.
Department of Digital Humanities, University of Cologne
Hosted by DH-Cologne
www.dhhumanist.org
Submit to: humanist@dhhumanist.org
Date: 2025-02-17 06:02:57+00:00
From: Toija Cinque <toija.cinque@deakin.edu.au>
Subject: CFP 'Data Care: A Humanities and Social Sciences Approach to Debiasing Large Language Models'
Dear Colleagues,
This is a call for papers for a special issue of Information,
Communication and Society ‘Data Care: A Humanities and Social Sciences
Approach to Debiasing Large Language Models’
Large Language Models (LLMs) are AI systems trained on vast datasets to
generate, understand, and process human language and expression,
enabling applications like chatbots, translation, and content creation.
Much research on LLMs is led by computer scientists focused on debiasing
data to build fairer models, while humanities and social science
scholars remain underrepresented in shaping AI’s decision-making
processes. Computational research often addresses ‘data loss’ and ‘data
deficiencies’ through data-centric AI approaches, whereas scholars from
media studies, anthropology, and political science for instance take a
social-centric approach to critique AI’s role in reinforcing historical
inequalities through data extraction and algorithmic governance. These
critiques, however, while important, rarely translate into
co-constructive decision-making to build datasets that are transparent,
equitable, and representative of the Majority World.
Emergent research from the Global South highlights AI’s potential to
challenge traditional gatekeepers, oppressive regimes, and patriarchal
norms, fostering a more hopeful perspective on LLM-powered innovations.
The rise of diverse LLMs—such as OpenAI’s GPT (USA), DeepSeek, Qwen
(China), Mistral (France), and Matilda (Australia)—demands a
cross-cultural approach to AI development. These models reflect
different linguistic, ethical, and socio-political contexts,
underscoring the need for a humanities and social sciences analysis
around what constitutes localized training data, multilingual
adaptability, and culturally aware governance that moves beyond
resistance toward rational optimism.
This special issue seeks to engage humanities and social science
scholars committed to improving the decision-making of AI by focusing on
debiasing strategies around notions of authenticity, provenance,
representation, and inclusion in data capture and curation. Centred on
the concept of Data Care, it promotes ethical, inclusive, and
community-driven data ecosystems guided by the CARE principles:
Collective Benefit, Authority to Control, Responsibility, and Ethics.
Moving beyond critique, this issue fosters interdisciplinary dialogue on
equitable AI development and invites contributions on replicable
strategies to debias and diversify LLMs from a cross-cultural perspective.
Themes and Topics
This special issue seeks papers that move beyond critique to actively
shape the development of Large Language Models (LLMs), through a
humanities and social sciences led approach to debiasing and
diversification. Contributions can take theoretical, empirical, or
cross-cultural approaches, particularly from Global South and Indigenous
contexts. The focus can be on:
* Creative Methods--case studies, comparative, and experimental
methodologies for inclusive dataset training, curation, annotation, and
governance.
* Equitable and Representative Training Data--strategies for
integrating cultural, linguistic, and epistemic diversity in LLMs,
addressing biases in dataset construction.
* Politics of Inclusion and Exclusion--analyses of geopolitical and
corporate-driven data exclusions, as well as creative activist
interventions that challenge algorithmic control, and strives to build
new forms of inclusive standards.
* ‘Rational optimism’ Approaches--collaborative approaches between
humanities scholars, social scientists, and computer scientists to shape
AI from within, working with stakeholders on the ground striving to
optimize AI innovations and address chronic data deficits to build
sustainable solutions.
* Operationalizing the CARE Principles--theoretical and empirical
research on embedding Collective Benefit, Authority to Control,
Responsibility, and Ethics into LLM development.
We invite interdisciplinary and cross-cultural comparative papers that
propose actionable pathways for fairer, more culturally responsive AI
systems.
Submission Guidelines
We invite contributions from humanities and social sciences scholars
including but not limited to media and communications studies,
anthropology, historians, cultural studies, STS, AI ethics, political
sciences, and design. We particularly encourage submissions from
researchers and practitioners based in the Global South.
Deadlines & Key Dates:
* EXTENDED ABSTRACT Submission Deadline: Friday 28 March 2025
Please send 1000-1200 words (including references) to
llmdatacare@gmail.com<mailto:llmdatacare@gmail.com>
Include the name(s) of the author(s);
The affiliation(s) and address(es) of the author(s);
The e-mail address, and telephone number(s) of the corresponding
author:
* NOTIFICATION of Accepted Abstracts to develop as full papers:
Monday 14 April 2025
* FULL PAPER DRAFT Submission: Friday 2 May 2025
* ACCEPTED Papers are invited to attend a Workshop and co-read each
other’s paper for feedback: Wednesday 11 June 2025, University of
Utrecht and hybrid.
* REVISION Deadline: 26 November 2025
* PUBLICATION Date: 2026
All submissions will undergo a double-blind peer review process. The
editorial team of Information, Communication & Society (ICS) has
expressed interest in a full Special Issue proposal comprising
approximately ten articles. We invite scholars to contribute their work
for consideration, with the potential for inclusion in the proposed
issue, pending final approval from the journal
For inquiries, please contact Toija Cinque
llmdatacare@gmail.com<mailto:llmdatacare@gmail.com>
We look forward to your contributions to this important conversation on
ensuring AI systems reflect and serve diverse cultural and creative
perspectives!
Guest Editors
Payal Arora, Professor of Inclusive AI Cultures, Utrecht University
p.arora@uu.nl<mailto:p.arora@uu.nl>
Toija Cinque, Associate Professor, Communications (Digital Media),
Deakin University
toija.cinque@deakin.edu.au<mailto:toija.cinque@deakin.edu.au>
Baohua Zhou, Professor and Associate Dean Director of the New Media
Communication Program, Founding Director of Computational and AI
Communication Research Center, Fudan University
zhoubaohua@yeah.net<mailto:zhoubaohua@yeah.net>
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