# It is not "accuracy vs. explainability" -- we need both for trustworthy AI systems

arXiv: https://arxiv.org/abs/2212.11136 · HTML: https://mlsanity.com/paper/2212.11136/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 16 Dec 2022.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples](https://mlsanity.com/paper/1708.05493/) (2017)
- [Data-driven Advice for Applying Machine Learning to Bioinformatics Problems](https://mlsanity.com/paper/1708.05070/) (2017)
- [Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations](https://mlsanity.com/paper/2110.10980/) (2021)

## Built on by (most cited first, 5 of 5)

- [Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects](https://mlsanity.com/paper/2304.08275/) (2023)
- [Structured Reasoning for Fairness: A Multi-Agent Approach to Bias Detection in Textual Data](https://mlsanity.com/paper/2503.00355/) (2025)
- [zIA: a GenAI-powered local auntie assists tourists in Italy](https://mlsanity.com/paper/2407.11830/) (2024)
- [SHARP: Social Harm Analysis via Risk Profiles for Measuring Inequities in Large Language Models](https://mlsanity.com/paper/2601.21235/) (2026)
- [Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoption](https://mlsanity.com/paper/2608.15181/) (2026)

## Abstract (arXiv)

We are witnessing the emergence of an AI economy and society where AI technologies are increasingly impacting health care, business, transportation and many aspects of everyday life. Many successes have been reported where AI systems even surpassed the accuracy of human experts. However, AI systems may produce errors, can exhibit bias, may be sensitive to noise in the data, and often lack technical and judicial transparency resulting in reduction in trust and challenges in their adoption. These recent shortcomings and concerns have been documented in scientific but also in general press such as accidents with self driving cars, biases in healthcare, hiring and face recognition systems for people of color, seemingly correct medical decisions later found to be made due to wrong reasons etc. This resulted in emergence of many government and regulatory initiatives requiring trustworthy and ethical AI to provide accuracy and robustness, some form of explainability, human control and oversight, elimination of bias, judicial transparency and safety. The challenges in delivery of trustworthy AI systems motivated intense research on explainable AI systems (XAI). Aim of XAI is to provide human understandable information of how AI systems make their decisions. In this paper we first briefly summarize current XAI work and then challenge the recent arguments of accuracy vs. explainability for being mutually exclusive and being focused only on deep learning. We then present our recommendations for the use of XAI in full lifecycle of high stakes trustworthy AI systems delivery, e.g. development, validation and certification, and trustworthy production and maintenance.