Abstract

Algorithmic recommender systems are on the rise in various societal domains, including journalism. While they offer great promise by making useful selections of large content pools, they raise various ethical and societal concerns due to their alleged lack of transparency, diversity and agency. Especially in the news context, this has serious implications because access to information is crucial in democratic societies. In this article we empirically explore the idea of algorithmic recommender personae as a productive socio-technical solution to these problems. We present the results from a two-phased qualitative study with Dutch and Belgian news readers (N = 27) to 1) co-design potential news recommender personae by inductively discerning core news reading motivations and relevant features, and 2) evaluate the most promising personae on their usefulness. Results highlight three distinct recommender personae (Expert, Challenger and Unwinder) that correspond with news consumers’ most salient reading motivations. We conclude that, in an increasingly automated future, allowing users more control and including them when designing recommender systems is key. With this study we hope that media organizations take up the challenge towards developing human-centered and responsible algorithmic systems that serve the public good.

摘要

算法推荐系统在包括新闻在内的各个社会领域都在兴起。虽然它们通过对大型内容池进行有用的选择来提供巨大的希望，但由于据称缺乏透明度、多样性和代理，它们引发了各种道德和社会问题。尤其是在新闻背景下，这具有严重的影响，因为获取信息在民主社会中至关重要。在本文中，我们经验性地探索算法推荐人角色的想法，作为对这些问题的有效社会技术解决方案。我们展示了与荷兰和比利时新闻读者进行的两阶段定性研究的结果（N = 27）到 1）通过归纳识别核心新闻阅读动机和相关特征来共同设计潜在的新闻推荐者角色，以及 2）评估最有希望的角色的有用性。结果突出显示了与新闻消费者最突出的阅读动机相对应的三个不同的推荐人角色（专家、挑战者和展开者）。我们得出的结论是，在自动化程度越来越高的未来，在设计推荐系统时允许用户进行更多控制并包括他们是关键。通过这项研究，我们希望媒体组织迎接挑战，开发以人为本和负责任的算法系统，为公共利益服务。