# Do you comply with AI? -- Personalized explanations of learning algorithms and their impact on employees' compliance behavior

arXiv: https://arxiv.org/abs/2002.08777 · HTML: https://mlsanity.com/paper/2002.08777/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 11 of 11)

- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- [Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making](https://mlsanity.com/paper/2204.06916/) (2022)
- ["There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making](https://mlsanity.com/paper/2205.05758/) (2022)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice](https://mlsanity.com/paper/2310.02108/) (2023)
- [Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer](https://mlsanity.com/paper/2207.00497/) (2022)
- [Factors that influence the adoption of human-AI collaboration in clinical decision-making](https://mlsanity.com/paper/2204.09082/) (2022)
- [Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations](https://mlsanity.com/paper/2208.11408/) (2022)
- [Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence](https://mlsanity.com/paper/2109.13827/) (2021)
- [Investigating the Role of Explainability and AI Literacy in User Compliance](https://mlsanity.com/paper/2406.12660/) (2024)
- [Exploring Human-AI Collaboration Using Mental Models of Early Adopters of Multi-Agent Generative AI Tools](https://mlsanity.com/paper/2510.06224/) (2025)

## 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.