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Accurately Predictions
09 August 2021

Myth: AI can accurately predict and optimize human behavior

With technological advances in the field of AI and a growing amount of behavioral data that employees produce in their day-to-day routine, so-called people analytics tools have become a topic of public debate. These tools capture and analyze the behavioral data of employees, combine it with business data and offer employees and their manager’s insights into work routines, performance and potential. Based on this, people analytics promises to objectify and optimize employee-related decisions. Managers, therefore, place high expectations on these tools, especially with a growing number of employees who work from home and move outside their spatial control.

Myth

AI can accurately predict and optimize human behavior.

AI can indeed predict probabilities of human actions based on historical data. However, the accuracy of these predictions depends heavily on the data quality and is by no means error-free. As the behavior of humans in the workplace is complex and cannot always be quantified and measured, the current generation of AI can support people management in a limited area only and will certainly not make human managers obsolete.

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Material

Presentation Slides
KEY LITERATURE

Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U. & Redzepi, A. (2021). The Dark Sides of People Analytics: Reviewing the Perils for Organisations and Employees. European Journal of Information Systems, ahead of print, 1-26.

Tursunbayeva, A., Di Lauro, S. & Pagliari, C. (2018). People analytics—A Scoping Review of Conceptual Boundaries and Value Propositions. International Journal of Information Management, 43, 224–247.

ADDITIONAL READINGS

AlgorithmWatch. (2020). Positionen zum Einsatz von KI im Personalmanagement. Rechte und Autonomie von Beschäftigten stärken – Warum Gesetzgeber, Unternehmen und Betriebsräte handeln müssen.

BMAS. (2021). Gesetz zur Förderung der Betriebsratswahlen und der Betriebsratsarbeit in einer digitalen Arbeitswelt (Betriebsrätemodernisierungsgesetz).
European Commission.

 
Gal, U., Jensen, T. B. & Stein, M.-K. (2020). Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Information and Organization, 30(2), 1-15.

Hammermann, A. & Thiele, C. (2019). People Analytics: Evidenzbasiert Entscheidungsfindung im Personalmanagement, IW-Report, No. 35/2019. Köln: Institut der deutschen Wirtschaft (IW). 

Leonardi, P. M. (2021). COVID‐19 and the New Technologies of Organizing: Digital Exhaust, Digital Footprints, and Artificial Intelligence in the Wake of Remote Work. Journal of Management Studies, 58(1), 249-253.

Netflix. (2020). Vorprogrammierte Diskriminierung [Video].

Nielsen, C. & McCullough, N. (2018). How People Analytics Can Help You Change Process, Culture, and Strategy. Harvard Business Review.

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group.  

Rasmussen, T. & Ulrich, D. (2015). Learning from Practice: How HR Analytics avoids being a Management Fad. Organizational Dynamics, 44(3), 236–242.

Stieler, W. (2021). Die Vermessung der Arbeit. MIT Technology Review, 4/2021, 22-27.

Thieltges, A. (2020). Machine Learning Anwendungen in der betrieblichen Praxis – Praktische Empfehlungen zur betrieblichen Mitbestimmung. Mitbestimmungspraxis, Nr. 33, Düsseldorf: Institut für Mitbestimmung und Unternehmensführung.

Tursunbayeva, A., Pagliari, C., Di Lauro, S. & Antonelli, G. (2021). The ethics of people analytics: risks, opportunities and recommendations, Personnel Review, ahead of print.

Zweig, K. (2019). Ein Algorithmus hat kein Taktgefühl: Wo künstliche Intelligenz sich irrt, warum uns das betrifft und was wir dagegen tun können. Heyne Verlag.
UNICORN IN THE FIELD

AlgorithmWatch

About the authors

Sonja Köhne | HIIG

Sonja Köhne

Researcher, University of Hamburg / Associated Doctoral Researcher, Humboldt Institute for Internet & Society

Sonja is an associated doctoral researcher in the group Innovation, Entrepreneurship & Society at HIIG and researcher at the University of Hamburg. At HIIG, she currently supports the research project Artificial Intelligence & Knowledge Work, funded by the German Federal Ministry for Labour and Social Affairs. Her research focuses, among other things, on the digitalization of processes in human resource management and its impact on employees. In particular, she is interested in so-called people analytics applications. Prior to her role as a researcher, Sonja spent five years working as an HR practitioner in the field of HR information systems. 

@sonjaxko

Miriam Klöpper

Researcher in the project “Anonymous Predictive People Analytics (AnyPPA)” at FZI Forschungszentrum Informatik, Berlin

Miriam is a research associate at the FZI Forschungszentrum Informatik Berlin/ Karlsruhe. She is currently supervising the BMBF-funded project Anonymous Predictive People Analytics. She focuses on the topic of the future of work and considers the social and ethical implications of the increasing use of AI in the workplace, as well as the development of co-determination and equal opportunities in the workplace. 

@kloeppermiriam

This post represents the view of the author and does not necessarily represent the view of the institute itself. For more information about the topics of these articles and associated research projects, please contact info@hiig.de.

Sonja Köhne

Doctoral Researcher: Innovation, Entrepreneurship & Society

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Du siehst Eisenbahnschienen. Die vielen verschiedenen Abzweigungen symbolisieren die Entscheidungsmöglichkeiten von Künstlicher Intelligenz in der Gesellschaft. Manche gehen nach oben, unten, rechts. Manche enden auch in Sackgassen. Englisch: You see railway tracks. The many different branches symbolise the decision-making possibilities of artificial intelligence and society. Some go up, down, to the right. Some also end in dead ends.

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