# Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2110.02707 · HTML: https://mlsanity.com/paper/2110.02707/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [Group-Based Privacy Preservation Techniques for Process Mining](https://mlsanity.com/paper/2105.11983/) (2021)
- [Privacy and Confidentiality in Process Mining -- Threats and Research Challenges](https://mlsanity.com/paper/2106.00388/) (2021)
- [PRIPEL: Privacy-Preserving Event Log Publishing Including Contextual Information](https://mlsanity.com/paper/2006.12856/) (2020)
- [Mine Me but Don't Single Me Out: Differentially Private Event Logs for Process Mining](https://mlsanity.com/paper/2103.11739/) (2021)
- [Privacy-Preserving Continuous Event Data Publishing](https://mlsanity.com/paper/2105.11991/) (2021)

## Built on by (most cited first, 4 of 4)

- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Achieving Group Fairness through Independence in Predictive Process Monitoring](https://mlsanity.com/paper/2412.04914/) (2024)
- [Revolutionizing Process Mining: A Novel Architecture for ChatGPT Integration and Enhanced User Experience through Optimized Prompt Engineering](https://mlsanity.com/paper/2405.10689/) (2024)
- [Design of a Quality Management System based on the EU Artificial Intelligence Act](https://mlsanity.com/paper/2408.04689/) (2024)

## Abstract (arXiv)

The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks. Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, transparency, accountability, and confidentiality requirements. These processes involve multiple steps, hand-offs, re-works, and human-in-the-loop oversight. In this paper, we demonstrate that process mining can provide a useful framework for gaining fact-based visibility to AI compliance process execution, surfacing compliance bottlenecks, and providing for an automated approach to analyze, remediate and monitor uncertainty in AI regulatory compliance processes.