@inproceedings{castelli-moreau-ph-d-2022-cycle,
title = "The Cycle of Trust and Responsibility in Outsourced {AI}",
author = "Castelli, Maximilian and
Moreau, Linda C.",
editor = "Verma, Apurv and
Pruksachatkun, Yada and
Chang, Kai-Wei and
Galstyan, Aram and
Dhamala, Jwala and
Cao, Yang Trista",
booktitle = "Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)",
month = jul,
year = "2022",
address = "Seattle, U.S.A.",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.trustnlp-1.4/",
doi = "10.18653/v1/2022.trustnlp-1.4",
pages = "43--48",
abstract = "Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming must-have capabilities. According to a 2019 Forbes Insights Report, {\textquotedblleft}seventy-nine percent [of executives] agree that AI is already having a transformational impact on workflows and tools for knowledge workers, but only 5{\%} of executives consider their companies to be industry-leading in terms of taking advantage of AI-powered processes.{\textquotedblright} (Forbes 2019) A major reason for this may be a shortage of on-staff expertise in AI/ML. This paper explores the intertwined issues of trust, adoption, training, and ethics of outsourcing AI development to a third party. We describe our experiences as a provider of outsourced natural language processing (NLP). We discuss how trust and accountability co-evolve as solutions mature from proof-of-concept to production-ready."
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%0 Conference Proceedings
%T The Cycle of Trust and Responsibility in Outsourced AI
%A Castelli, Maximilian
%A Moreau, Linda C.
%Y Verma, Apurv
%Y Pruksachatkun, Yada
%Y Chang, Kai-Wei
%Y Galstyan, Aram
%Y Dhamala, Jwala
%Y Cao, Yang Trista
%S Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, U.S.A.
%F castelli-moreau-ph-d-2022-cycle
%X Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming must-have capabilities. According to a 2019 Forbes Insights Report, “seventy-nine percent [of executives] agree that AI is already having a transformational impact on workflows and tools for knowledge workers, but only 5% of executives consider their companies to be industry-leading in terms of taking advantage of AI-powered processes.” (Forbes 2019) A major reason for this may be a shortage of on-staff expertise in AI/ML. This paper explores the intertwined issues of trust, adoption, training, and ethics of outsourcing AI development to a third party. We describe our experiences as a provider of outsourced natural language processing (NLP). We discuss how trust and accountability co-evolve as solutions mature from proof-of-concept to production-ready.
%R 10.18653/v1/2022.trustnlp-1.4
%U https://aclanthology.org/2022.trustnlp-1.4/
%U https://doi.org/10.18653/v1/2022.trustnlp-1.4
%P 43-48
Markdown (Informal)
[The Cycle of Trust and Responsibility in Outsourced AI](https://aclanthology.org/2022.trustnlp-1.4/) (Castelli & Moreau, TrustNLP 2022)
ACL
- Maximilian Castelli and Linda C. Moreau. 2022. The Cycle of Trust and Responsibility in Outsourced AI. In Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022), pages 43–48, Seattle, U.S.A.. Association for Computational Linguistics.