Do you comply with AI? -- Personalized explanations of learning algorithms and their impact on employees' compliance behavior
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Abstract (arXiv)
Machine Learning algorithms are technological key enablers for artificial intelligence (AI). Due to the inherent complexity, these learning algorithms represent black boxes and are difficult to comprehend, therefore influencing compliance behavior. Hence, compliance with the recommendations of such artifacts, which can impact employees' task performance significantly, is still subject to research - and personalization of AI explanations seems to be a promising concept in this regard. In our work, we hypothesize that, based on varying backgrounds like training, domain knowledge and demographic characteristics, individuals have different understandings and hence mental models about the learning algorithm. Personalization of AI explanations, related to the individuals' mental models, may thus be an instrument to affect compliance and therefore employee task performance. Our preliminary results already indicate the importance of personalized explanations in industry settings and emphasize the importance of this research endeavor.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 5; 2023: 2; 2024: 1; 2025: 2.
11 papers.
- 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 80
- 2022Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Makingcited by 28
- 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Makingcited by 19
- 2025Explainability in Practice: A Survey of Explainable NLP Across Various Domainscited by 11
- 2023Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advicecited by 8
- 2022Training Novices: The Role of Human-AI Collaboration and Knowledge Transfercited by 5
- 2022Factors that influence the adoption of human-AI collaboration in clinical decision-makingcited by 4
- 2022Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizationscited by 3
- 2021Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligencecited by 2
- 2024Investigating the Role of Explainability and AI Literacy in User Compliancecited by 2
- 2025Exploring Human-AI Collaboration Using Mental Models of Early Adopters of Multi-Agent Generative AI Toolscited by 0
- 2025Exploring Human-AI Collaboration Using Mental Models of Early Adopters of Multi-Agent Generative AI Tools10 Sep 2025
- 2025Explainability in Practice: A Survey of Explainable NLP Across Various Domains2 Feb 2025
- 2024Investigating the Role of Explainability and AI Literacy in User Compliance18 Jun 2024
- 2023Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice3 Oct 2023
- 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations4 Feb 2023
- 2022Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations24 Aug 2022
- 2022Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer1 Jul 2022
- 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making11 May 2022
- 2022Factors that influence the adoption of human-AI collaboration in clinical decision-making19 Apr 2022
- 2022Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making14 Apr 2022
- 2021Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence28 Sep 2021