@inproceedings{caselli-etal-2021-guiding,
title = "Guiding Principles for Participatory Design-inspired Natural Language Processing",
author = "Caselli, Tommaso and
Cibin, Roberto and
Conforti, Costanza and
Encinas, Enrique and
Teli, Maurizio",
editor = "Field, Anjalie and
Prabhumoye, Shrimai and
Sap, Maarten and
Jin, Zhijing and
Zhao, Jieyu and
Brockett, Chris",
booktitle = "Proceedings of the 1st Workshop on NLP for Positive Impact",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4posimpact-1.4",
doi = "10.18653/v1/2021.nlp4posimpact-1.4",
pages = "27--35",
abstract = "We introduce 9 guiding principles 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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<abstract>We introduce 9 guiding principles 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.</abstract>
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%0 Conference Proceedings
%T Guiding Principles for Participatory Design-inspired Natural Language Processing
%A Caselli, Tommaso
%A Cibin, Roberto
%A Conforti, Costanza
%A Encinas, Enrique
%A Teli, Maurizio
%Y Field, Anjalie
%Y Prabhumoye, Shrimai
%Y Sap, Maarten
%Y Jin, Zhijing
%Y Zhao, Jieyu
%Y Brockett, Chris
%S Proceedings of the 1st Workshop on NLP for Positive Impact
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F caselli-etal-2021-guiding
%X We introduce 9 guiding principles 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.
%R 10.18653/v1/2021.nlp4posimpact-1.4
%U https://aclanthology.org/2021.nlp4posimpact-1.4
%U https://doi.org/10.18653/v1/2021.nlp4posimpact-1.4
%P 27-35
Markdown (Informal)
[Guiding Principles for Participatory Design-inspired Natural Language Processing](https://aclanthology.org/2021.nlp4posimpact-1.4) (Caselli et al., NLP4PI 2021)
ACL