# Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworks

arXiv: https://arxiv.org/abs/2507.10208 · HTML: https://mlsanity.com/paper/2507.10208/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 82 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 22 of 22)

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- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores](https://mlsanity.com/paper/2002.08035/) (2020)
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- [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023)
- [Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis](https://mlsanity.com/paper/2309.12368/) (2023)
- [Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative](https://mlsanity.com/paper/2103.04725/) (2021)
- [FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System](https://mlsanity.com/paper/1908.00681/) (2019)
- [How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study](https://mlsanity.com/paper/2309.10108/) (2023)
- [Iris: A Conversational Agent for Complex Tasks](https://mlsanity.com/paper/1707.05015/) (2017)
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- [Snowy: Recommending Utterances for Conversational Visual Analysis](https://mlsanity.com/paper/2110.04323/) (2021)
- [Discovering the Sweet Spot of Human-Computer Configurations: A Case Study in Information Extraction](https://mlsanity.com/paper/1909.07065/) (2019)
- [Ethical Dimensions of Visualization Research](https://mlsanity.com/paper/1811.07271/) (2018)
- [EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation](https://mlsanity.com/paper/2112.07858/) (2021)
- [Paths Explored, Paths Omitted, Paths Obscured: Decision Points & Selective Reporting in End-to-End Data Analysis](https://mlsanity.com/paper/1910.13602/) (2019)
- ["If I Had All the Time in the World": Ophthalmologists' Perceptions of Anchoring Bias Mitigation in Clinical AI Support](https://mlsanity.com/paper/2303.03981/) (2023)
- [Perceptual Pat: A Virtual Human System for Iterative Visualization Design](https://mlsanity.com/paper/2303.06537/) (2023)
- [Data Quality, Mismatched Expectations, and Moving Requirements: The Challenges of User-Centred Dashboard Design](https://mlsanity.com/paper/2209.06363/) (2022)

## Abstract (arXiv)

Research into explainable artificial intelligence (XAI) for data analysis tasks suffer from a large number of contradictions and lack of concrete design recommendations stemming from gaps in understanding the tasks that require AI assistance. In this paper, we drew on multiple fields such as visual analytics, cognition, and dashboard design to propose a method for categorising and comparing XAI studies under three dimensions: what, why, and who. We identified the main problems as: inadequate descriptions of tasks, context-free studies, and insufficient testing with target users. We propose that studies should specifically report on their users' domain, AI, and data analysis expertise to illustrate the generalisability of their findings. We also propose study guidelines for designing and reporting XAI tasks to improve the XAI community's ability to parse the rapidly growing field. We hope that our contribution can help researchers and designers better identify which studies are most relevant to their work, what gaps exist in the research, and how to handle contradictory results regarding XAI design.