CASELLI, Tommaso, Roberto CIBIN, Costanza CONFORTI, Enrique ENCINAS and Maurizio TELI. Guiding Principles for Participatory Design-inspired Natural Language Processing. In 978-1-954085-69-5. NLP4POSIMPACT 2021: THE 1ST WORKSHOP ON NLP FOR POSITIVE IMPACT. STROUDSBURG: ASSOC COMPUTATIONAL LINGUISTICS-ACL, 2021, p. 27-35.
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Basic information
Original name Guiding Principles for Participatory Design-inspired Natural Language Processing
Authors CASELLI, Tommaso, Roberto CIBIN, Costanza CONFORTI, Enrique ENCINAS and Maurizio TELI.
Edition STROUDSBURG, NLP4POSIMPACT 2021: THE 1ST WORKSHOP ON NLP FOR POSITIVE IMPACT, p. 27-35, 9 pp. 2021.
Publisher ASSOC COMPUTATIONAL LINGUISTICS-ACL
Other information
Type of outcome Proceedings paper
Confidentiality degree is not subject to a state or trade secret
UT WoS 000696679700004
Tags International impact, Reviewed
Changed by Changed by: Roberto Cibin, Ph.D., učo 245894. Changed: 23/8/2022 15:38.
Abstract
We introduce 9 guiding principles1 to integrate Participatory Design (PD) methods in the development of Natural Language Processing (NLP) systems. The adoption of PD methods by NLP will help to alleviate issues concerning the development of more democratic, fairer, less-biased technologies to process natural language data. This short paper is the outcome of an ongoing dialogue between designers and NLP experts and adopts a non-standard format following previous work by Traum (2000); Bender (2013); Abzianidze and Bos (2019). Every section is a guiding principle. While principles 1-3 illustrate assumptions and methods that inform community-based PD practices, we used two fictional design scenarios (Encinas and Blythe, 2018), which build on top of situations familiar to the authors, to elicit the identification of the other 6. Principles 4-6 describes the impact of PD methods on the design of NLP systems, targeting two critical aspects: data collection & annotation, and the deployment& evaluation. Finally, principles 7-9 guide a new reflexivity of the NLP research with respect to its context, actors and participants, and aims. We hope this guide will offer inspiration and a road-map to develop a new generation of PD-inspired NLP.
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