# A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

arXiv: https://arxiv.org/abs/1811.11839 · HTML: https://mlsanity.com/paper/1811.11839/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 16 of 16)

- ["Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction](https://mlsanity.com/paper/2210.03735/) (2022)
- [Temporal Dependencies in Feature Importance for Time Series Predictions](https://mlsanity.com/paper/2107.14317/) (2021)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy](https://mlsanity.com/paper/2008.12735/) (2020)
- [DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods](https://mlsanity.com/paper/2109.05800/) (2021)
- [Evaluating the Correctness of Explainable AI Algorithms for Classification](https://mlsanity.com/paper/2105.09740/) (2021)
- [Time Series Model Attribution Visualizations as Explanations](https://mlsanity.com/paper/2109.12935/) (2021)
- [Why do explanations fail? A typology and discussion on failures in XAI](https://mlsanity.com/paper/2405.13474/) (2024)
- [Some Critical and Ethical Perspectives on the Empirical Turn of AI Interpretability](https://mlsanity.com/paper/2109.09586/) (2021)
- [Explainable Misinformation Detection Across Multiple Social Media Platforms](https://mlsanity.com/paper/2203.11724/) (2022)
- [Visual Explanations with Attributions and Counterfactuals on Time Series Classification](https://mlsanity.com/paper/2307.08494/) (2023)
- [Exploring The Role of Local and Global Explanations in Recommender Systems](https://mlsanity.com/paper/2109.13301/) (2021)
- [Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts](https://mlsanity.com/paper/2205.05749/) (2022)
- [PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification](https://mlsanity.com/paper/2102.05917/) (2021)
- [How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations](https://mlsanity.com/paper/2312.04379/) (2023)
- [Human-in-the-Loop User Feedback Affects Perceived Accuracy and Trust, but Task Subjectivity Matters](https://mlsanity.com/paper/2607.17548/) (2026)

## Abstract (arXiv)

The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence applications used in everyday life. Explainable intelligent systems are designed to self-explain the reasoning behind system decisions and predictions, and researchers from different disciplines work together to define, design, and evaluate interpretable systems. However, scholars from different disciplines focus on different objectives and fairly independent topics of interpretable machine learning research, which poses challenges for identifying appropriate design and evaluation methodology and consolidating knowledge across efforts. To this end, this paper presents a survey and framework intended to share knowledge and experiences of XAI design and evaluation methods across multiple disciplines. Aiming to support diverse design goals and evaluation methods in XAI research, after a thorough review of XAI related papers in the fields of machine learning, visualization, and human-computer interaction, we present a categorization of interpretable machine learning design goals and evaluation methods to show a mapping between design goals for different XAI user groups and their evaluation methods. From our findings, we develop a framework with step-by-step design guidelines paired with evaluation methods to close the iterative design and evaluation cycles in multidisciplinary XAI teams. Further, we provide summarized ready-to-use tables of evaluation methods and recommendations for different goals in XAI research.