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Over time, the loss of human expertise caused by AI use can impair the quality of that very AI – in the worst case, insidiously and unnoticed. This is the finding of a new study by researchers from the University of Passau and Arizona State University, which was recently published in the renowned journal Academy of Management Review.
Tech companies around the world are making headlines with job cuts, often citing the increased use of artificial intelligence (AI). A new study by researchers from the University of Passau and Arizona State University in the United States shows that these decisions may be short-sighted.
Knowledge learned by machines can help organisations accumulate experience, for example, when experienced employees retire. However, machine learning models are based on historical training data and therefore become outdated when the reality they represent changes. The need to update these models and the resulting cycle of “use and repair” poses risks for companies.
In the study “Fading Memories: The Role of Machine Learning in Organizational Knowledge Depreciation”, Professor Jin Gerlach from the University of Passau and Professor Don Lange from Arizona State University show how companies can get caught up in this cycle:
- AI takes over: While AI systems perform their tasks, such as quality control in a manufacturing process, employees use the relevant expertise less frequently, forget it or leave the company altogether.
- Knowledge loss occurs: This way, human expertise is lost while new hires gain less expertise when tasks are taken over by AI.
- AI models become outdated: Ageing AI models must be updated with the help of new training data, model variables must be checked for relevance, or incorrect predictions by the model must be assessed by experts. Such updates require human expertise, which is increasingly lacking.
This means that AI models can become knowledge traps as they age. ‘Lost human expertise can impair the quality of AI models over time – potentially in a creeping and unnoticed manner,’ warns Passau-based information systems expert Professor Jin Gerlach. ‘If employees uncritically internalise the predictions and decisions of an outdated AI model, this can further corrupt their human knowledge.’ In their study, the authors emphasise that the long-term successful use of AI can only be achieved if companies simultaneously preserve human expertise.
Effective use of AI requires human expertise
‘Our findings point to longer-term and unintended consequences of the use of AI in organisations,’ says Professor Gerlach. ‘They emphasise the need to retain human expertise, as failing to do so can jeopardise the effective use of AI systems over time.’ According to the researchers, the corporate decisions mentioned at the beginning appear in a different light. This is because they neglect human know-how – and thus weaken AI in the long term.
The study was recently published online in the journal Academy of Management Review, one of the most prestigious scientific journals in the field of management. The work is a conceptual theory study – it is not based on new empirical data, but combines existing findings from organisational research and computer science to form a new model. The authors derive a process theory that explains how and why a phenomenon – in this case, organisational knowledge loss due to AI – occurs.
About the authors
The lead author is Professor Jin Gerlach, who holds the Chair of Data and Information Management at the University of Passau. His research focuses on technology-driven changes in business and society. Co-author Professor Donald Lange researches and teaches ethical issues in management at the W.P. Carey School of Business at Arizona State University in the USA.
The two authors met during Professor Lange's research visit at the University of Passau. He spent a year as a Mercator Fellow at the Passau DFG Research Training Group 2720 “Digital Platform Ecosystems” to investigate the interaction between public values and digital platforms. The Mercator Fellowship is a scholarship awarded by the German Research Foundation (DFG) to researchers who are intensively involved in a project.
This text was machine-translated from German.
Professor Jin Gerlach
Chair of Data and Information Management
University of Passau
Email: Jin.Gerlach@uni-passau.de
Gerlach, J. P. und Lange, D.: Fading Memories: The Role of Machine Learning in Organizational Knowledge Depreciation, Academy of Management Review.
https://journals.aom.org/doi/10.5465/amr.2024.0408
Professor Jin Gerlach, Chair of Data and Information Management.
Quelle: University of Passau
Copyright: University of Passau
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