# A systematic review and taxonomy of explanations in decision support and recommender systems

arXiv: https://arxiv.org/abs/2006.08672 · HTML: https://mlsanity.com/paper/2006.08672/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 30 of 45)

- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [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)
- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs](https://mlsanity.com/paper/2101.09824/) (2021)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [What Makes a Good Explanation?: A Harmonized View of Properties of Explanations](https://mlsanity.com/paper/2211.05667/) (2022)
- [Explainability in Music Recommender Systems](https://mlsanity.com/paper/2201.10528/) (2022)
- [Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs](https://mlsanity.com/paper/2102.08540/) (2021)
- [An Explainable Decision Support System for Predictive Process Analytics](https://mlsanity.com/paper/2207.12782/) (2022)
- [Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System](https://mlsanity.com/paper/2304.00969/) (2023)
- [Unpacking Human-AI interactions: From interaction primitives to a design space](https://mlsanity.com/paper/2401.05115/) (2024)
- [Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework](https://mlsanity.com/paper/2105.10197/) (2021)
- [Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models](https://mlsanity.com/paper/2203.16073/) (2022)
- [Co-audit: tools to help humans double-check AI-generated content](https://mlsanity.com/paper/2310.01297/) (2023)
- [How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey](https://mlsanity.com/paper/2403.14496/) (2024)
- [Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goals](https://mlsanity.com/paper/2302.06609/) (2023)
- [Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System](https://mlsanity.com/paper/2305.17034/) (2023)
- [Two-Stage Holistic and Contrastive Explanation of Image Classification](https://mlsanity.com/paper/2306.06339/) (2023)
- [Trust and Transparency in Recommender Systems](https://mlsanity.com/paper/2304.08094/) (2023)
- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)
- [Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support](https://mlsanity.com/paper/2507.00535/) (2025)
- [Visualization for Recommendation Explainability: A Survey and New Perspectives](https://mlsanity.com/paper/2305.11755/) (2023)
- [The Case Against Explainability](https://mlsanity.com/paper/2305.12167/) (2023)
- [On the Multiple Roles of Ontologies in Explainable AI](https://mlsanity.com/paper/2311.04778/) (2023)
- [Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments](https://mlsanity.com/paper/2105.02825/) (2021)
- [Knowledge-based XAI through CBR: There is more to explanations than models can tell](https://mlsanity.com/paper/2108.10363/) (2021)
- [Multi-Objective Recommender Systems: Survey and Challenges](https://mlsanity.com/paper/2210.10309/) (2022)
- [Justification of Recommender Systems Results: A Service-based Approach](https://mlsanity.com/paper/2211.03452/) (2022)

## Abstract (arXiv)

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.