# Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

arXiv: https://arxiv.org/abs/2206.10610 · HTML: https://mlsanity.com/paper/2206.10610/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 85 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 16 of 16)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Towards Explainable Deep Neural Networks (xDNN)](https://mlsanity.com/paper/1912.02523/) (2019)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)
- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [Explainable Software Analytics](https://mlsanity.com/paper/1802.00603/) (2018)
- [On Cognitive Preferences and the Plausibility of Rule-based Models](https://mlsanity.com/paper/1803.01316/) (2018)
- [A hybrid machine learning framework for analyzing human decision making through learning preferences](https://mlsanity.com/paper/1906.01233/) (2019)

## Built on by (most cited first, 21 of 21)

- [Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study](https://mlsanity.com/paper/2304.08861/) (2023)
- [Explainable AI Insights for Symbolic Computation: A case study on selecting the variable ordering for cylindrical algebraic decomposition](https://mlsanity.com/paper/2304.12154/) (2023)
- [Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins](https://mlsanity.com/paper/2207.09106/) (2022)
- [Beware of "Explanations" of AI](https://mlsanity.com/paper/2504.06791/) (2025)
- [Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models](https://mlsanity.com/paper/2504.16969/) (2025)
- [An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology](https://mlsanity.com/paper/2311.02205/) (2023)
- [Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables](https://mlsanity.com/paper/2308.03915/) (2023)
- [QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems](https://mlsanity.com/paper/2309.10293/) (2023)
- [Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems](https://mlsanity.com/paper/2404.17508/) (2024)
- [A Unified Approach to Extract Interpretable Rules from Tree Ensembles via Integer Programming](https://mlsanity.com/paper/2407.00843/) (2024)
- [An Analysis of the New EU AI Act and A Proposed Standardization Framework for Machine Learning Fairness](https://mlsanity.com/paper/2510.01281/) (2025)
- [Hypothesis Testing and Machine Learning: Interpreting Variable Effects in Deep Artificial Neural Networks using Cohen's f2](https://mlsanity.com/paper/2302.01407/) (2023)
- [Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships](https://mlsanity.com/paper/2310.15999/) (2023)
- [Is Machine Learning Unsafe and Irresponsible in Social Sciences? Paradoxes and Reconsidering from Recidivism Prediction Tasks](https://mlsanity.com/paper/2311.06537/) (2023)
- [Detecting Brain Tumors through Multimodal Neural Networks](https://mlsanity.com/paper/2402.00038/) (2024)
- [Integrating Explainable AI in Medical Devices: Technical, Clinical and Regulatory Insights and Recommendations](https://mlsanity.com/paper/2505.06620/) (2025)
- [IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems](https://mlsanity.com/paper/2506.21310/) (2025)
- [Explaining AI Without Code: A User Study on Explainable AI](https://mlsanity.com/paper/2602.11159/) (2025)
- [Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators](https://mlsanity.com/paper/2608.26148/) (2026)
- [Verifying the Linear Representation Hypothesis: How Interpretable Are Vision SAEs?](https://mlsanity.com/paper/2609.35020/) (2026)
- [Large Language Models are Approximate Survival Estimators](https://mlsanity.com/paper/2609.38181/) (2026)

## Abstract (arXiv)

Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher performance are often based on more complex algorithms and therefore lack explainability and vice versa. However, there is little to no empirical evidence of this tradeoff from an end user perspective. We aim to provide empirical evidence by conducting two user experiments. Using two distinct datasets, we first measure the tradeoff for five common classes of machine learning algorithms. Second, we address the problem of end user perceptions of explainable artificial intelligence augmentations aimed at increasing the understanding of the decision logic of high-performing complex models. Our results diverge from the widespread assumption of a tradeoff curve and indicate that the tradeoff between model performance and explainability is much less gradual in the end user's perception. This is a stark contrast to assumed inherent model interpretability. Further, we found the tradeoff to be situational for example due to data complexity. Results of our second experiment show that while explainable artificial intelligence augmentations can be used to increase explainability, the type of explanation plays an essential role in end user perception.