# How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice

arXiv: https://arxiv.org/abs/2107.04427 · HTML: https://mlsanity.com/paper/2107.04427/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Towards Explainable Artificial Intelligence](https://mlsanity.com/paper/1909.12072/) (2019)
- [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019)
- [Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs](https://mlsanity.com/paper/2101.09824/) (2021)
- [Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems](https://mlsanity.com/paper/1806.07552/) (2018)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)

## Built on by (most cited first, 9 of 9)

- [Achieving Diversity in Counterfactual Explanations: a Review and Discussion](https://mlsanity.com/paper/2305.05840/) (2023)
- [Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions](https://mlsanity.com/paper/2210.02795/) (2022)
- [Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods](https://mlsanity.com/paper/2202.03759/) (2022)
- [Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem](https://mlsanity.com/paper/2306.13885/) (2023)
- [Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI](https://mlsanity.com/paper/2507.12913/) (2025)
- [Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal](https://mlsanity.com/paper/2302.03180/) (2023)
- [X-SYS: A Reference Architecture for Interactive Explanation Systems](https://mlsanity.com/paper/2602.12748/) (2026)
- [Responsible AI in Business](https://mlsanity.com/paper/2602.13244/) (2026)
- [Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design](https://mlsanity.com/paper/2608.07091/) (2026)

## Abstract (arXiv)

Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning community has paid little attention to the stakeholder whose needs are rather studied in the human-computer interface community. Therefore, organizations that want or need to provide this explainability are confronted with the selection of an appropriate method for their use case. In this paper, we argue there is a need for a methodology to bridge the gap between stakeholder needs and explanation methods. We present our ongoing work on creating this methodology to help data scientists in the process of providing explainability to stakeholders. In particular, our contributions include documents used to characterize XAI methods and user requirements (shown in Appendix), which our methodology builds upon.