AI meets labor market: Exploring the link between automation and skills

Colombo, Emilio; Mercorio, Fabio; Mezzanzanica, Mario

doi:10.1016/j.infoecopol.2019.05.003

Cited by 99 publications

(69 citation statements)

References 18 publications

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“…The data related to labor market issues are characterized by high volume and velocity of updated content which is explainable due to the continuously evolving character of the labor market [2]. That is why their analysis requires different and usually advanced methods for content analysis [8]. Through the current study and by identifying the appropriate methods adopted to extract labor market knowledge (e.g., machine learning algorithms, statistical measures and methods, etc.)…”

Section: Introductionmentioning

confidence: 94%

“…Taking into consideration that the collected data from job portals need data analysis methods, we can understand that several existing Artificial Intelligence algorithms can be used to extract knowledge from digital sources for labor market purposes [8]. We have to note that Matturro et al [16] showed that the most appropriate sources to identify soft skills for software engineering sector are job advertisements, surveys (online or by e-mail) and interviews.…”

Section: A Related Workmentioning

confidence: 99%

“…The most popular are: (i) Indeed, 6 (ii) CareerBuilder, 7 and (iii) Monster. 8 Monster is a leading private Internet recruitment site which has been used as a web source for several research tasks to analyze online job vacancy data. It has been used since 2004 [18] as an online source for analyzing job advertisements for company branding identification.…”

Section: B Labor Market Analyticsmentioning

confidence: 99%

“…More specifically, aiming to provide analytics almost in real time, labor market stakeholders retrieve content from digital sources such as social networks, online job portals or Q&A and collaborative sites. In order to handle the collected digital content practitioners need to adopt techniques such as data analysis and artificial intelligence methods [8]. We can note that the well-known approaches of sentiment analysis could find implication to labor market analytics, as a need of labor market is the detection of soft skills which can relate to emotional intelligence/sentiment analysis [17].…”

Section: B Labor Market Analyticsmentioning

confidence: 99%

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Extracting Knowledge From On-Line Sources for Software Engineering Labor Market: A Mapping Study

et al. 2019

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Software engineering is a continuously evolving sector and the demands of the related labor market result in a wide variety of job openings, ranging from developers to customer service positions. Thus, there is a need to continuously monitor labor market trends using data and analytics. Both employers and employees can benefit by capturing emerging trends which can facilitate continuous learning and training in new technologies, support of better matching between a job offer and the ideal candidate and expertise detection. To fulfill these needs, the results of labor market analytics need to reach the stakeholders timely and accurately. However, often delays occur, which stem from time-consuming approaches based on collecting data from traditional sources, such as questionnaires or interviews. Recently, researchers started leveraging content from digital sources, which are easily accessed and contain a wealth of information. This paper presents the results of a Systematic Mapping Study on digital sources that can be used to address the data analytics needs of the labor market. It provides a multifaceted categorization of the issues involved in the analysis of digital sources of the software engineering labor market. It aims to identify digital labor market sources for data retrieval which are appropriate for employers and employees analytics. Additionally, it aims to connect different skill types, needs and goals of labor market with the utilization of digital sources and data analysis methods. In total 86 primary studies were selected and each one was evaluated and classified aiming to identify the: (a) digital sources that are used for labor market analytics; (b) type of skills they examine; (c) methods which are used to utilize the raw digital content; (d) goals for which every primary study is conducted; (e) beneficiaries (stakeholder) of the results; and (f) time trends for all the above.

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Section: Introductionmentioning

confidence: 94%

Section: A Related Workmentioning

confidence: 99%

Section: B Labor Market Analyticsmentioning

confidence: 99%

Section: B Labor Market Analyticsmentioning

confidence: 99%

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Extracting Knowledge From On-Line Sources for Software Engineering Labor Market: A Mapping Study

et al. 2019

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“…The emergence of "Big Data" has allowed a deeper look at the mismatch issues between the demand for skills and their supply. The emergence and development of artificial intelligence technologies enable the better assess the labor market status [6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Equilibrium of Labor Market: New Security Instruments in the Context of Digitalization

et al. 2021

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The actual methods of labor market analysis are based on outdated technologies for collecting information and do not consider the competencies available in the CV and in demand by vacancies. In order to obtain reliable current information on the balance of the workforce quality, as the carrier of certain competencies, and market requirements, the method is proposed for determining the degree of their consistency through the ratio of competencies available to applicants and those requested by employers. The proposed methodology, based on big data technologies, uses artificial intelligence as the main toolkit which makes it possible to quickly and efficiently collect, process and visualize the obtained data, which makes it possible to conduct its further qualitative analysis in the context of the proposed / demanded competencies, professions, regions and types of economic activities. As a practical interpretation of the proposed methodology, the paper analyzes the degree of consistency of existing / demanded competencies in the context of the regions of Belarus.

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Defining artificial intelligence as a policy problem: A discourse network analysis from Germany

Lemke,

Trein,

Varone

2024

European Policy Analysis

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Scholars agree that digital technologies such as artificial intelligence (AI) pose a political challenge. In this article, we study empirically how different actors in the German political system define AI as a policy problem. We use an original data set of 6421 statements by representatives of political parties, interest groups, scientific experts, and public officials in parliamentary debates, government consultations, and quality newspapers. Through Discourse Network Analysis and quantitative text analyses we show that most actors define AI as technology (innovation) policy and link it to government operations, international cooperation, and macroeconomics. Although they are present, consumer protection, labor, and education seem to be less important policy issues concerning AI. The results imply that the capacity of the national government to reduce problem definition uncertainty and to steer the political agenda is difficult and that most actors focus on technological innovation rather than civil rights‐related aspects.

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AI meets labor market: Exploring the link between automation and skills

Cited by 99 publications

References 18 publications

Extracting Knowledge From On-Line Sources for Software Engineering Labor Market: A Mapping Study

Extracting Knowledge From On-Line Sources for Software Engineering Labor Market: A Mapping Study

Equilibrium of Labor Market: New Security Instruments in the Context of Digitalization

Defining artificial intelligence as a policy problem: A discourse network analysis from Germany

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