"It Might be Technically Impressive, But It's Practically Useless to Us": Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry

arXiv - CS - Social and Information Networks Pub Date : 2024-09-18 DOI:arxiv-2409.12000

Qing Xiao, Xianzhe Fan, Felix M. Simon, Bingbing Zhang, Motahhare Eslami

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Abstract

Recently, an increasing number of news organizations have integrated artificial intelligence (AI) into their workflows, leading to a further influx of AI technologists and data workers into the news industry. This has initiated cross-functional collaborations between these professionals and journalists. While prior research has explored the impact of AI-related roles entering the news industry, there is a lack of studies on how cross-functional collaboration unfolds between AI professionals and journalists. Through interviews with 17 journalists, 6 AI technologists, and 3 AI workers with cross-functional experience from leading news organizations, we investigate the current practices, challenges, and opportunities for cross-functional collaboration around AI in today's news industry. We first study how journalists and AI professionals perceive existing cross-collaboration strategies. We further explore the challenges of cross-functional collaboration and provide recommendations for enhancing future cross-functional collaboration around AI in the news industry.

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"技术上可能令人印象深刻，但实际上对我们毫无用处"：新闻行业围绕人工智能开展跨职能合作的实践、挑战和机遇

最近，越来越多的新闻机构将人工智能（AI）整合到其工作流程中，导致人工智能技术人员和数据工作者进一步涌入新闻行业。虽然之前的研究已经探讨了人工智能相关角色进入新闻行业的影响，但缺乏对人工智能专业人员与新闻记者之间如何开展跨职能合作的研究。通过采访 17 名记者、6 名人工智能技术专家和 3 名具有跨职能经验的领先新闻机构的人工智能工作者，我们调查了当今新闻行业围绕人工智能开展跨职能合作的现行做法、挑战和机遇。我们首先研究了记者和人工智能专业人员如何看待现有的跨部门合作战略。我们进一步探讨了跨职能合作所面临的挑战，并为加强新闻行业未来围绕人工智能的跨职能合作提出了建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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arXiv - CS - Social and Information Networks

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