# Do We Need Explainable AI in Companies? Investigation of Challenges, Expectations, and Chances from Employees' Perspective

arXiv: https://arxiv.org/abs/2210.03527 · HTML: https://mlsanity.com/paper/2210.03527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [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)
- [GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning](https://mlsanity.com/paper/2012.11905/) (2020)

## Abstract (arXiv)

Companies' adoption of artificial intelligence (AI) is increasingly becoming an essential element of business success. However, using AI poses new requirements for companies and their employees, including transparency and comprehensibility of AI systems. The field of Explainable AI (XAI) aims to address these issues. Yet, the current research primarily consists of laboratory studies, and there is a need to improve the applicability of the findings to real-world situations. Therefore, this project report paper provides insights into employees' needs and attitudes towards (X)AI. For this, we investigate employees' perspectives on (X)AI. Our findings suggest that AI and XAI are well-known terms perceived as important for employees. This recognition is a critical first step for XAI to potentially drive successful usage of AI by providing comprehensible insights into AI technologies. In a lessons-learned section, we discuss the open questions identified and suggest future research directions to develop human-centered XAI designs for companies. By providing insights into employees' needs and attitudes towards (X)AI, our project report contributes to the development of XAI solutions that meet the requirements of companies and their employees, ultimately driving the successful adoption of AI technologies in the business context.