# A Co-design Study for Multi-Stakeholder Job Recommender System Explanations

arXiv: https://arxiv.org/abs/2309.05507 · HTML: https://mlsanity.com/paper/2309.05507/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Visual Classification via Description from Large Language Models](https://mlsanity.com/paper/2210.07183/) (2022)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS](https://mlsanity.com/paper/1912.06059/) (2019)
- [XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2009.04796/) (2020)
- [XAI Handbook: Towards a Unified Framework for Explainable AI](https://mlsanity.com/paper/2105.06677/) (2021)
- [Job Recommender Systems: A Review](https://mlsanity.com/paper/2111.13576/) (2021)
- [Providing Explanations for Recommendations in Reciprocal Environments](https://mlsanity.com/paper/1807.01227/) (2018)
- [Users are the North Star for AI Transparency](https://mlsanity.com/paper/2303.05500/) (2023)

## Built on by (most cited first, 4 of 4)

- [Explainable Multi-Stakeholder Job Recommender Systems](https://mlsanity.com/paper/2410.00654/) (2024)
- [Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations](https://mlsanity.com/paper/2409.15971/) (2024)
- [Design Requirements for Human-Centered Graph Neural Network Explanations](https://mlsanity.com/paper/2405.06917/) (2024)
- [Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs](https://mlsanity.com/paper/2509.09522/) (2025)

## Abstract (arXiv)

Recent legislation proposals have significantly increased the demand for eXplainable Artificial Intelligence (XAI) in many businesses, especially in so-called `high-risk' domains, such as recruitment. Within recruitment, AI has become commonplace, mainly in the form of job recommender systems (JRSs), which try to match candidates to vacancies, and vice versa. However, common XAI techniques often fall short in this domain due to the different levels and types of expertise of the individuals involved, making explanations difficult to generalize. To determine the explanation preferences of the different stakeholder types - candidates, recruiters, and companies - we created and validated a semi-structured interview guide. Using grounded theory, we structurally analyzed the results of these interviews and found that different stakeholder types indeed have strongly differing explanation preferences. Candidates indicated a preference for brief, textual explanations that allow them to quickly judge potential matches. On the other hand, hiring managers preferred visual graph-based explanations that provide a more technical and comprehensive overview at a glance. Recruiters found more exhaustive textual explanations preferable, as those provided them with more talking points to convince both parties of the match. Based on these findings, we describe guidelines on how to design an explanation interface that fulfills the requirements of all three stakeholder types. Furthermore, we provide the validated interview guide, which can assist future research in determining the explanation preferences of different stakeholder types.