Assessing Policy II: Governance-Choice Method

Latzer, Michael; Saurwein, Florian; Just, Natascha; Bulck, Hilde Van den; Puppis, Manuel; Donders, Karen; Audenhove, Leo Van

doi:10.1007/978-3-030-16065-4_32

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Regarding control, the typology of harms allows us to reflect upon suitable governance responses (Latzer et al, 2019) by exploring incentives for social media platforms to reduce harms by means of platform selfregulation. Industry self-regulation is unsuitable in cases where harms are also indistinguishable from the commercial interests of industry players.…”

Section: Discussionmentioning

confidence: 99%

Automated Trouble: The Role of Algorithmic Selection in Harms on Social Media Platforms

Saurwein

Spencer-Smith

2021

MaC

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Social media platforms like Facebook, YouTube, and Twitter have become major objects of criticism for reasons such as privacy violations, anticompetitive practices, and interference in public elections. Some of these problems have been associated with algorithms, but the roles that algorithms play in the emergence of different harms have not yet been systematically explored. This article contributes to closing this research gap with an investigation of the link between algorithms and harms on social media platforms. Evidence of harms involving social media algorithms was collected from media reports and academic papers within a two-year timeframe from 2018 to 2019, covering Facebook, YouTube, Instagram, and Twitter. Harms with similar casual mechanisms were grouped together to inductively develop a typology of algorithmic harm based on the mechanisms involved in their emergence: (1) algorithmic errors, undesirable, or disturbing selections; (2) manipulation by users to achieve algorithmic outputs to harass other users or disrupt public discourse; (3) algorithmic reinforcement of pre-existing harms and inequalities in society; (4) enablement of harmful practices that are opaque and discriminatory; and (5) strengthening of platform power over users, markets, and society. Although the analysis emphasizes the role of algorithms as a cause of online harms, it also demonstrates that harms do not arise from the application of algorithms alone. Instead, harms can be best conceived of as socio-technical assemblages, composed of the use and design of algorithms, platform design, commercial interests, social practices, and context. The article concludes with reflections on possible governance interventions in response to identified socio-technical mechanisms of harm. Notably, while algorithmic errors may be fixed by platforms themselves, growing platform power calls for external oversight.

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Section: Discussionmentioning

confidence: 99%

Automated Trouble: The Role of Algorithmic Selection in Harms on Social Media Platforms

Saurwein

Spencer-Smith

2021

MaC

Self Cite

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“…Reducing the algorithmically fueled risks introduced above is a goal of risk-based regulatory approaches (Latzer & Just, 2020). Such governance modes include statutory regulation (e.g., the GDPR in the EU), market solutions, and selfregulation of the industry (Latzer, Saurwein, et al, 2019;Latzer & Just, 2020;Saurwein et al, 2015;Seyfert, 2021). Despite statutory regulation aiming at increasing users' sovereignty over their own data, many internet users wish for more control over algorithms (Festic, 2020).…”

Section: Regulation Of Algorithmic Risks: Self-help Strategiesmentioning

confidence: 99%

Coping with Algorithmic Risks

Kappeler

Festic

Latzer

et al. 2023

jdsr

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Algorithmic selection is omnipresent in various domains of our online everyday lives: it ranks our search results, curates our social media news feeds, or recommends videos to watch and music to listen to. This widespread application of algorithmic selection on the internet can be associated with risks like feeling surveilled (S), feeling exposed to distorted information (D), or feeling like one is using the internet too excessively (O). One way in which internet users can cope with such algorithmic risks is by applying self-help strategies such as adjusting their privacy settings (Sstrat), double-checking information (Dstrat), or deliberately ignoring automated recommendations (Ostrat). This article determines the association of the theoretically derived factors risk awareness (1), personal risk affectedness (2), and algorithm skills (3) with these self-help strategies. The findings from structural equation modelling on survey data representative for the Swiss online population (N2018=1,202) show that personal affectedness by algorithmic risks, awareness of algorithmic risks and algorithm skills are associated with the use of self-help strategies. These results indicate that besides implementing statutory regulation, policy makers have the option to encourage internet users’ self-help by increasing their awareness of algorithmic risks, clarifying how such risks affect them personally, and promoting their algorithm skills.

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“…To implement the different approaches, the question arises as to the appropriate extent of the role of the state and the corresponding governance instruments. For risk-based approaches, a governance choice procedure (Latzer et al, 2019) offers a case-specific decision-making aid.…”

Section: Governance In a Complex Digital Worldmentioning

confidence: 99%

Assessing Policy II: Governance-Choice Method

Cited by 9 publications

References 18 publications

Automated Trouble: The Role of Algorithmic Selection in Harms on Social Media Platforms

Automated Trouble: The Role of Algorithmic Selection in Harms on Social Media Platforms

Coping with Algorithmic Risks

Digital Trinity – Controllable Evolution – Everyday Religion. Characteristics of the Socio-Technical Transformation of Digitalization

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