Towards platform observability

Rieder, Bernhard; Hofmann, Jeanette

doi:10.14763/2020.4.1535

Cited by 55 publications

(53 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Yet, we also found YouTube clearly prioritises certain channels (source diversity) over time and across steps, which provided important insights into how YouTube operationalises "authoritativeness" in practice. US channels dominated across queries, down the chains, and over time, highlighting the cultural dominance of the US on YouTube (Rieder et al, 2020). Our data also suggests that YouTube makes decisions to categorise certain topics deemed societally significant and truth-oriented enough for heavyheaded platform intervention (e.g.…”

Section: Resultsmentioning

confidence: 61%

What’s ‘Up Next’? Investigating Algorithmic Recommendations on Youtube Across Issues and Over Time

Matamoros-Fernández

Gray

Bartolo

et al. 2021

SPIR

View full text Add to dashboard Cite

YouTube’s ‘up next’ feature algorithmically suggests videos to watch after a video that is currently playing. This feature has been criticised for limiting users’ exposure to diverse media content and information sources; meanwhile, YouTube has reported that they have implemented technical and policy changes to address these concerns. Yet, there is limited data to support either the existing concerns or YouTube’s claims. Drawing on the concept of platform observability, this paper combines computational and qualitative methods to investigate the types of content YouTube’s ‘up next’ feature amplifies over time, using three search terms associated with sociocultural issues where concerns have been raised about YouTube’s role: ‘coronavirus’, ‘feminism’ and ‘beauty’. Over six weeks, we collected the videos (and their metadata) that were highly ranked in the search results for each keyword, as well as the top-ranked recommendations associated with each video, repeating the exercise for three steps in the recommendation chain. We then examined patterns in the recommended videos (and channels) for each query and their variation over time. We found evidence of YouTube's stated efforts to boost ‘authoritative’ media outlets, but at the same time, misleading and controversial content continues to be recommended. We also found that while algorithmic recommendations offer diversity in videos over time, there are clear ‘winners’ at the channel level that are given a visibility boost in YouTube’s 'up next' feature. These impacts were attenuated differently depending on the nature of the search topic.

show abstract

Section: Resultsmentioning

confidence: 61%

What’s ‘Up Next’? Investigating Algorithmic Recommendations on Youtube Across Issues and Over Time

Matamoros-Fernández

Gray

Bartolo

et al. 2021

SPIR

View full text Add to dashboard Cite

show abstract

“…Zumal insbesondere aus demokratietheoretischer Sicht die Annahme einer absoluten Herrschaft wenig überzeugend scheintdas gilt für institutionelle Settings und muss auch auf technisierte Umgebungen übertragen werden. Es muss also wesentlich präziser danach gefragt werden, ob und in welchem Ausmaß in Umgebungen algorithmischer Systeme Formen der Kritik und Befragung ermöglicht werden oder nicht, und ob Algorithmen darauf Einfluss haben, was und wie kritisiert und befragt werden kann (Rieder und Hofmann 2020).…”

Section: Demokratie Als Auflösung Formal-rationalistischer Begründungsbestrebungenunclassified

Das Ende des Politischen? Demokratische Politik und Künstliche Intelligenz

Koster

2021

Z Politikwiss

View full text Add to dashboard Cite

ZusammenfassungIn jüngster Zeit findet innerhalb der politiktheoretischen Forschung eine vermehrte Auseinandersetzung mit algorithmenbasierten Systemen statt. Diese ist geprägt von der Behauptung einer neuen algorithmischen Regierungsweise, die aufgrund ihrer reduktionistisch-formalen Logik sowohl plurale Sinnzusammenhänge untergräbt als auch die individuelle Entfaltung reflexiver Urteilsbildung unterminiert. Entgegen dieser Annahmen argumentiert der vorliegende Beitrag, dass der Einsatz dieser digitalen Technologien im politischen Kontext nicht zwangsläufig in eine post-politische Verfasstheit von Gesellschaft münden muss. Algorithmische Systeme lassen sich als spezifische epistemische Verfahren verstehen, deren operativer Gebrauch symbolischer Inputs zwar einer schließenden, ontologisierenden Logik folgt und für sich genommen kontingenzreduzierende und latent anti-politische Wirkungen zeitigt. Demokratische Gesellschaften zeichnen sich aber hinsichtlich ihrer kontingenztheoretischen Verfahren gerade dadurch aus, dass ihre befragende Logik eine Inkorporation solcher Ontologisierungen ermöglicht. Es geht dann vielmehr darum, die Bedingungen ihrer Politisierung in den Blick zu nehmen.

show abstract

“…Legislations such as the EU Digital Services Act, or the Algorithmic Accountability Act and the Filter Bubble Transparency Act in the US, or the German Medienstaatsvertrag (State Media Treaty) have sparked heated public debates and received supports from certain political factions and stakeholder groups (Rieder & Hofmann, 2020). Moreover, crucial data are absent on public debates about AI governance in developing countries.…”

Section: Public Perception Toward Aimentioning

confidence: 99%

My Boss the Computer: A Bayesian analysis of socio-demographic and cross-cultural determinants of attitude toward the Non-Human Resource Management

Mantello¹,

Nguyen

et al. 2021

SSRN Journal

View full text Add to dashboard Cite

Human resource management technologies have moved from biometric surveillance to emotional artificial intelligence (AI) that monitor employees' engagement and productivity, analyze video interviews and CVs of job applicants. The rise of the US$20 billion emotional AI industry will transform the future workplace. Yet, besides no international consensus on the principles or standards for such technologies, there is a lack of cross-cultural research on future job seekers' attitude toward such use of AI technologies. This study collects a cross-sectional dataset of 1,015 survey responses of international students from 48 countries and 8 regions worldwide. A majority of the respondents (52%) are concerned about being managed by AI. Following the hypothetico-deductivist philosophy of science, we use the MCMC Hamiltonian approach and conduct a detailed comparison of 10 Bayesian network models with the PSIS-LOO method. We consistently find having a higher income, being male, majoring in business, and/or self-rated familiarity with AI correlate with a more positive view of emotional AI in the workplace. There is also a stark cross-cultural and cross-regional difference. Our analysis shows people from economically less developed regions (Africa, Oceania, Central Asia) tend to exhibit less concern for AI managers. And for East Asian countries, 64% of the Japanese, 56% of the South Korean, and 42% of the Chinese professed the trusting attitude. In contrast, an overwhelming majority of 75% of the European and Northern American possesses the worrying/neutral attitude toward being managed by AI. Regarding religion, Muslim students correlate with the most concern toward emotional AI in the workplace (β_Islam_Attitude's mean =-0.16, sd =0.10; β_Buddhism_Attitude's mean =-0.05, sd =0.07; β_Christian_Attitude 's mean = -0.10, sd= 0.09). When religiosity is higher, the correlation becomes stronger for Muslim and Buddhist 4 students. This paper adds a cross-cultural perspective to the literature, which is currently skewed toward country-specific and profession-specific samples.

show abstract

Towards platform observability

Cited by 55 publications

References 47 publications

What’s ‘Up Next’? Investigating Algorithmic Recommendations on Youtube Across Issues and Over Time

What’s ‘Up Next’? Investigating Algorithmic Recommendations on Youtube Across Issues and Over Time

Das Ende des Politischen? Demokratische Politik und Künstliche Intelligenz

My Boss the Computer: A Bayesian analysis of socio-demographic and cross-cultural determinants of attitude toward the Non-Human Resource Management

Contact Info

Product

Resources

About