36.2052, Confs: 1st Workshop on Linguistic & Informatic Insights on AI for Gender, Accessibility, and Inclusivity (Italy)

[The LINGUIST List]

LINGUIST List: Vol-36-2052. Thu Jul 03 2025. ISSN: 1069 - 4875.

Subject: 36.2052, Confs: 1st Workshop on Linguistic & Informatic Insights on AI for Gender, Accessibility, and Inclusivity (Italy)

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Date: 03-Jul-2025
From: Jacqueline Aiello [jaiello at unisa.it]
Subject: 1st Workshop on Linguistic & Informatic Insights on AI for Gender, Accessibility, and Inclusivity


1st Workshop on Linguistic & Informatic Insights on AI for Gender,
Accessibility, and Inclusivity
Short Title: LINFA

Location: Salerno, Italy
Contact: Jacqueline Aiello
Contact Email: jaiello at unisa.it
Meeting URL: https://sites.google.com/unisa.it/linfachitaly2025/

Linguistic Field(s): Applied Linguistics; Computational Linguistics;
Lexicography; Sociolinguistics; Text/Corpus Linguistics

Submission Deadline: 16-Jul-2025

Institutions across higher education, legislative bodies, the
judiciary, public broadcasting, and public health agencies exert
significant influence through their communication practices, yet they
often reproduce biases embedded in traditional linguistic conventions.
Recent global events have also emphasized the imperative for these
institutions to guarantee both the broad dissemination of information
and the protection of public well-being via accessible, unbiased,
representative, and inclusive messaging. Although the rapid expansion
of large language models offers promising avenues for automating
inclusive and accessible language use, these systems often mirror and
even exacerbate existing prejudices. Addressing these challenges
demands a transdisciplinary response: computer scientists and
linguists must collaborate to unpack the distinct and cross-linguistic
gender markings and manifestations of bias, as well as the features of
accessible language, all within a human-centered AI paradigm. By
anchoring AI training in a multilingual ‘gold standard’ derived from
rigorously curated linguistic resources, we can recalibrate and steer
automated outputs toward unbiased language, gender sensitivity, and
accessibility, thereby promoting equity in institutional discourse.
The LINFA: Linguistic & Informatic insights on AI for gender,
accessibility, and inclusivity workshop aims to translate our research
foundation into concrete, collaborative action by convening scholars
and practitioners from different disciplines to advance AI-driven
strategies for equitable, gender-inclusive, and accessible
institutional communication. We invite submissions that critically
examine existing language technologies, evaluate or propose novel
multilingual resources, explore the sociolinguistic dimensions of
equitable, gender-sensitive, and accessible discourse across different
languages, and showcase prototype applications or empirical
assessments of user impact in real-world contexts.
The workshop will explore a range of themes in support of its goal –
to foster an interdisciplinary dialogue for enhancing equity and
inclusivity in cross-linguistic institutional messaging through
principled, multilingual, human-centered AI solutions – including, but
not limited to, the following:
 - gender bias and inequitable messaging in institutional
communication
 - human-centered AI paradigms and bias-mitigation strategies
 - design and curation of multilingual resources for inclusive
training data
 - cross-linguistic and sociolinguistic analyses of gender marking,
bias, and/or prejudicial discourses
 - critical evaluation of LLMs and other language technologies for
automated inclusive language
 - obstacles to comprehensibility and accessibility in institutional
texts
 - plain-language and accessibility best practices for institutional
communication
 - prototype applications of AI tools in real-world institutional
contexts
 - empirical methodologies for assessing user reception, impact,
comprehension, and/or trust
 - frameworks and metrics for evaluating and guiding AI-driven
inclusive messaging
 - organizational, legal, and policy considerations for sustainable
deployment of inclusive AI systems



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