Algorithms as regulatory objects

Seyfert, Robert

doi:10.1080/1369118x.2021.1874035

Cited by 13 publications

(11 citation statements)

References 44 publications

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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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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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“…The issue of credibility in data collection and storage has become more pressing due to the proliferation of ICTs and the implementation of macro-level data protection policies across the EU (Kuner et al , 2015). Data protection legislation at the macro level arose in response to a crisis brought on by advances in technology and the widespread abuse of individuals’ private data (Seyfert, 2022). In this information privacy context, employees judge the credibility of their own organization’s privacy policy statement, as it manifests in the organizational practices of collecting, processing and storing personal information.…”

Section: The Information Privacy Calculus At the Workplacementioning

confidence: 99%

“…Metzger and Flanagin (2013) note that the complexity of determining credibility in today’s digital workplace stems from the fact that there are so many potential “targets” of credibility evaluation, all of which are active at once. As our starting point, we take the legislation of the European scenario and the organizational practices resulting from the regulation of data algorithms (Seyfert, 2022). The practices are oriented toward the EU’s General Data Protection Regulation (GDPR, 2023) principles (Albrecht, 2016).…”

Section: The Information Privacy Calculus At the Workplacementioning

confidence: 99%

Credibility and trust of information privacy at the workplace in Slovakia. The use of intuition

Svenson

Mikušková

Launer

2023

JICES

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Purpose Employees may feel overwhelmed with information privacy choices and have difficulties understanding what they are committing to in the digital workplace. This paper aims to analyze the role of different thinking styles for effort reduction, such as the use of intuition, when employees make decisions about the credibility and trustworthiness of workplace information privacy issues in Slovakia. While the General Data Protection Regulation sets precise requirements for valid consent, organizations are classified as data controllers and are subject to credibility judgments by their employees. Design/methodology/approach Data was collected from 230 employees in Slovakia using a survey questionnaire. Quantitative analysis using SPSS was conducted to describe employees thinking preferences when judging the credibility of information privacy in their organizations. Findings The survey participants revealed their perceived credibility and trust in personal data protection and thinking preferences. Unconscious thinking is the type of effort reduction often reported by participants, who perceive high credibility and trust in personal data protection. This study can help managers and data controllers in small- and medium-sized enterprises in reflecting about the way in which people use different thinking processes for decision-making about information privacy in their organizations. Research limitations/implications This study set out to explore how decision-making processes at the workplace relate to credibility of data practices. Focusing on the use of different types of intuition, the authors explored whether the preference for a specific decision-making style can explain the perceived credibility of data practices. The part of the workforce in the sample did not have a strict predisposition to use either intuitive or rational thinking. Practical implications The contribution provides scholars with an overview of the field of intuition, a field that is likely to grow given the challenges of digitalization for organizations, such as shitstorms, cyberattacks and whistleblowing. Originality/value The most well-known concepts from intuition research, e.g. the dual process theory, and practice are tested simultaneously, therewith contributing to the applied literature on domain-specific preferences for intuition and deliberation in decision-making.

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“…A positive approach builds on this work to develop and improve these governance regimes. Seyfert (2021) argues that regulatory processes co‐produce the algorithms they regulate, but AI‐specific regulations are a new development, often lacking ‘teeth’ or meaningful enforcement. To the extent that formal political channels are open to us, sociologists can contribute to the development of the nascent regulatory regimes being established to govern AI and algorithms in their respective jurisdictions or in an international forum.…”

Section: The Future Of Inequality and Sociology's Responsementioning

confidence: 99%

Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates

Zajko

2022

Sociology Compass

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Artificial intelligence (AI) and algorithmic systems have been criticized for perpetuating bias, unjust discrimination, and contributing to inequality. Artificial intelligence researchers have remained largely oblivious to existing scholarship on social inequality, but a growing number of sociologists are now addressing the social transformations brought about by AI. Where bias is typically presented as an undesirable characteristic that can be removed from AI systems, engaging with social inequality scholarship leads us to consider how these technologies reproduce existing hierarchies and the positive visions we can work towards. I argue that sociologists can help assert agency over new technologies through three kinds of actions: (1) critique and the politics of refusal;(2) fighting inequality through technology; and (3) governance of algorithms. As we become increasingly dependent on AI and automated systems, the dangers of further entrenching or amplifying social inequalities have been well documented, particularly with the growing adoption of these systems by government agencies. However, public policy also presents some opportunities to restructure social dynamics in a positive direction, as long as we can articulate what we are trying to achieve, and are aware of the risks and

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Algorithms as regulatory objects

Cited by 13 publications

References 44 publications

Coping with Algorithmic Risks

Coping with Algorithmic Risks

Credibility and trust of information privacy at the workplace in Slovakia. The use of intuition

Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates

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