On the Democratic Role of News Recommenders

Helberger, Natali

doi:10.1080/21670811.2019.1623700

Cited by 223 publications

(149 citation statements)

References 32 publications

(21 reference statements)

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“…One expanding body of literature assesses the effects of news personalisation on users and society. The focal points here are the impact of algorithmic selection on diversity, and the potential of personalised recommendations to result in "filter bubbles" (Dylko et al 2017;Flaxman, Goel, and Rao 2016;Haim, Graefe, and Brosius 2018;Helberger 2019;M€ oller et al 2019;Pariser 2011;Zuiderveen Borgesius et al 2016), or expose users exclusively to similar opinions-"echo chambers" (Quattrociocchi, Scala, and Sunstein 2016;Sunstein 2009). Research on the effects of news personalisation typically sees users as passive receivers of information rather than active actors in the process of news personalisation.…”

Section: Introductionmentioning

confidence: 99%

User Perspectives on the News Personalisation Process: Agency, Trust and Utility as Building Blocks

et al. 2020

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With the increasing use of algorithms in news distribution, commentators warn about its possible impacts on the changing relationship between the news media and news readers. To understand the meaning of news personalisation strategies to users, we investigated how they currently experience news personalisation, perceive their role in the personalisation process, and envision increasing the utility of personalised news by giving users more agency and fostering trust. We conducted four focus groups with online news readers in Germany. For the analysis, grounded theory techniques were suitable due to their applicability in reconstructing user perspectives through their own experiences. We found that (1) users fail to distinguish between news personalisation and commercial targeting, which may negatively bias their perception; (2) there is a contradiction in how users perceive themselves as active participants in the process, but lack the means to exercise agency; (3) user concerns extend beyond privacy to what information they receive and their right to personal autonomy-a solution requires offering users the ability to dynamically adjust their "news interest profiles"; (4) while news personalisation strategies afford new opportunities for introducing reciprocity in the media-audience relationship, negotiating competing logics of journalistic, personal and algorithmic curation remains a challenge.

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Section: Introductionmentioning

confidence: 99%

User Perspectives on the News Personalisation Process: Agency, Trust and Utility as Building Blocks

et al. 2020

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“…While there have been discussions on the ethics of recommender systems and personalization (Bozdag & Timmermans, 2011;Helberger, 2019;Milano et al, 2020), and on how to fulfill main algorithmic principles such as fairness, transparency, accountability, accuracy, and privacy (algo:aware, 2018), in this hypothetical radical scenario, algorithms could even be exchanged, remixed, tested, plugged, and even sold or rented. On top of this opening, it is expected that not all users would have the knowledge to build their own algorithms.…”

Section: Algorithmic Sovereignty In Theorymentioning

confidence: 99%

Thinking Outside the Black-Box: The Case for “Algorithmic Sovereignty” in Social Media

Reviglio

Agosti

2020

Social Media + Society

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This article is an interdisciplinary critical analysis of personalization systems and the gatekeeping role of current mainstream social media. The first section presents a literature review of data-driven personalization and its challenges in social media. The second section sheds light on increasing concerns regarding algorithms’ ability to overtly persuade—and covertly manipulate—users for the sake of engagement, introducing the emergence of the exclusive ownership of behavioral modification through hyper-nudging techniques. The third section empirically analyzes users’ expectations and behaviors regarding such data-driven personalization to frame a conceptualization of users’ agency. The fourth section introduces the concept of “algorithmic sovereignty.” Current projects that aim to grant this algorithmic sovereignty highlight some potential applications. Together this novel theoretical framework and empirical applications suggest that, to preserve trust, social media should open their personalization algorithms to a social negotiation as the first step toward a more sustainable social media landscape. To decentralize the immense power of mainstream social media, guarantee a democratic oversight, and mitigate the unintended undesirable consequences of their algorithmic curation, public institutions and civil society could help in developing and researching public algorithms, fostering a collective awareness so as to eventually ensure a fair and accountable “algorithmic sovereignty.”

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“…The potential application of community well-being metrics to these systems illustrates the challenges around defining a community and choosing metrics. News recommendation algorithms can have societal consequences (Helberger 2019 ) but it is not clear how to manage such algorithms for community well-being. To begin with, there is no single community that consumes news, but many overlapping communities organized around different geographic regions and different topics (Reader and Hatcher 2011 , p. 3).…”

Section: Generalization To Other Domainsmentioning

confidence: 99%

Aligning AI Optimization to Community Well-Being

Stray¹

2020

Int. Journal of Com. WB

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This paper investigates incorporating community well-being metrics into the objectives of optimization algorithms and the teams that build them. It documents two cases where a large platform appears to have modified their system to this end. Facebook incorporated “well-being” metrics in 2017, while YouTube began integrating “user satisfaction” metrics around 2015. Metrics tied to community well-being outcomes could also be used in many other systems, such as a news recommendation system that tries to increase exposure to diverse views, or a product recommendation system that opstimizes for the carbon footprint of purchased products. Generalizing from these examples and incorporating insights from participatory design and AI governance leads to a proposed process for integrating community well-being into commercial AI systems: identify and involve the affected community, choose a useful metric, use this metric as a managerial performance measure and/or an algorithmic objective, and evaluate and adapt to outcomes. Important open questions include the best approach to community participation and the uncertain business effects of this process.

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On the Democratic Role of News Recommenders

Cited by 223 publications

References 32 publications

User Perspectives on the News Personalisation Process: Agency, Trust and Utility as Building Blocks

User Perspectives on the News Personalisation Process: Agency, Trust and Utility as Building Blocks

Thinking Outside the Black-Box: The Case for “Algorithmic Sovereignty” in Social Media

Aligning AI Optimization to Community Well-Being

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