Generation theory in HR practice

Nikitinsky, Nikita; Kachurina, Polina; Sergey, Shashev; Shamis, Evgeniya

doi:10.1145/3014087.3014126

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Discourse As Neural Network2

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2017

2023

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Cited by 5 publications

(2 citation statements)

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“…5 At present, the domain of graphics and image recognition appears to have benefited most from using neural networks. For example, a neural network model Word2Vector is used to analyze words in the form of vectors for identifying similar meanings in similar contexts (Nikitinsky et al 2016). 5 See, e.g.…”

Section: Online Discourse As Neural Networkmentioning

confidence: 99%

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Beyond Bureaucracy

Montargil¹,

Santos²

2017

Public Administration and Information Technology

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Section: Online Discourse As Neural Networkmentioning

confidence: 99%

“…While the effort to apply AI tools that analyze texts and argumentation (see, e.g., Riveret et al 2015;Nikitinsky et al 2016), the use of neural networks, in our view, is not a mathematical or computer science task alone. It is an inter-disciplinary challenge that could be overcome if three basic conditions are met.…”

Section: Discourse As Neural Networkmentioning

confidence: 99%

Beyond Bureaucracy

Montargil¹,

Santos²

2017

Public Administration and Information Technology

View full text Add to dashboard Cite

Converting the Outcomes of Citizens’ Discourses in the Cyberspace into Policy Inputs for More Democratic and Effective Government

Misnikov

Filatova

Chugunov

2017

Public Administration and Information Technology

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Responsible artificial intelligence in human resources management: a review of the empirical literature

et al. 2023

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As it is the case for many business processes and activities disciplines, artificial intelligence (AI) is increasingly integrated in human resources management (HRM). While AI has great potential to augment the HRM activities in organizations, automating the management of humans is not without risks and limitations. The identification of these risks is fundamental to promote responsible use of AI in HRM. We thus conducted a review of the empirical academic literature across disciplines on the affordances and responsible principles of AI in HRM. This is the first review of responsible AI in HRM that focuses solely on studies containing observations, measurements, and tests about this phenomenon. The multi-domain and multidisciplinary approach and empirical focus provides a better understanding of the reality of the development, study, and deployment of AI in HRM and sheds light on how these are conducted responsibly. We conclude with a call for research based on what we identified as the most needed and promising avenues.

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Generation theory in HR practice

Cited by 5 publications

References 3 publications

Beyond Bureaucracy

Beyond Bureaucracy

Converting the Outcomes of Citizens’ Discourses in the Cyberspace into Policy Inputs for More Democratic and Effective Government

Responsible artificial intelligence in human resources management: a review of the empirical literature

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