Market power and artificial intelligence work on online labour markets

Duch‐Brown, Néstor; Gómez-Herrera, Estrella; Mueller‐Langer, Frank; Tolan, Songül

doi:10.1016/j.respol.2021.104446

Cited by 28 publications

(11 citation statements)

References 40 publications

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“…As metrics with a focus on balance, we compute the inverse of the Herfindahl-Hirschman Index (HHI), which is commonly used in economics to measure market concentration [40], and the Shannon Index (H') [41], which is commonly used in ecology to measure biodiversity [42]. HHI is computed as follows:…”

Section: Diversity Indicatorsmentioning

confidence: 99%

Measuring and Fostering Diversity in Affective Computing Research

Hupont

Tolan

Frau

et al. 2024

IEEE Trans. Affective Comput.

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This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Our findings reveal a "leaky pipeline" in the field, with a low -albeit slowly increasing over the years-representation of women. They also show that academic institutions clearly dominate publications, ahead of industry and governmental centres. In terms of geography, most publications come from the USA, contributions from Latin America or Africa being almost non-existent. Lastly, we find that diversity in the characteristics of researchers (gender and geographic location) influences diversity in the topics. To conclude, we analyse initiatives that have been undertaken in other AI-related research communities to foster diversity, and recommend a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The diversity data collected in this work are publicly available, ensuring strict personal data protection and governance rules.

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Section: Diversity Indicatorsmentioning

confidence: 99%

Measuring and Fostering Diversity in Affective Computing Research

Hupont

Tolan

Frau

et al. 2024

IEEE Trans. Affective Comput.

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“…In one of these studies, Stephany, Kässi, Rani, and Lehdonvirta, (2021: 1) cited OLI as the way to measure the world's remote freelance platform users. Duch-Brown et al (2022) used OLI as a benchmarking parameter in their analysis with the data they obtained from the PPH (PeoplePerHour) platform to calculate the market power of platform users working in the field of artificial intelligence.…”

Section: The Online Labour Index (Oli)mentioning

confidence: 99%

The Effect of Macroeconomic Factors on Online Labor Platform Users: The Case of Turkey

Tuna

Karadaş

2023

Yönetim Ve Ekonomi Araştırmaları Dergisi

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This study examines the relationship between the online labour force and real economic factors. Since the Online labour index is one of the main determinants of the GIG economy, this study also reveals which macroeconomic variables the GIG economy is based on. The aim of the study is to determine the effect of real economic factors (unemployment, economic growth, inflation) on the count of online platform users. As a result of the econometric analysis conducted with Turkey's data set for the period of June 2017 -January 2021, it has been determined that real economic factors have a statistically significant effect on the count of online platform users. According to the results of the analysis, the increase in unemployment rate and economic growth will cause an increase in the count of online platform users, while increase in inflation will cause an opposite effect.

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“…As metrics with a focus on balance, we compute the inverse of the Herfindahl-Hirschman Index (HHI), which is commonly used in economics to measure market concentration [33], and the Shannon Index (H') [34], which is commonly used in ecology to measure biodiversity [35]. HHI is computed as follows:…”

Section: Diversity Indicatorsmentioning

confidence: 99%

Measuring and fostering diversity in Affective Computing research

Hupont¹,

Tolan²,

Frau³

et al. 2022

Preprint

Self Cite

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<p>This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. Our findings reveal notable correlations between different types of diversity, such as gender and institutional type, geography and topics, as well as topics and first author’s gender. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Finally, we analyse diversity initiatives that have been undertaken in other AI-related research communities to foster diversity, and conclude on a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The data collected in this work will be publicly available, ensuring strict personal data protection and governance rules.</p>

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Market power and artificial intelligence work on online labour markets

Cited by 28 publications

References 40 publications

Measuring and Fostering Diversity in Affective Computing Research

Measuring and Fostering Diversity in Affective Computing Research

The Effect of Macroeconomic Factors on Online Labor Platform Users: The Case of Turkey

Measuring and fostering diversity in Affective Computing research

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