Explainable AI (XAI)
Techniques that make an AI model's reasoning or outputs interpretable to people.
11 papers carry this themeView in grid →
In these papers
- 01A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?Ziming Wang · 2024A roadmap of XAI: explain to whom, when, what, and how.
- 02Article LIME-Mine: Explainable Machine Learning for User Behavior Analysis in IoT ApplicationsXiaobo Cai · 2024LIME-Mine: explainable ML for user-behaviour analysis (IoT).
- 03User Characteristics in Explainable AI: The Rabbit Hole of Personalization?Robert Nimmo · 2024User characteristics in explainable AI.
- 04Predicting the Need for XAI from High-Granularity Interaction DataVagner Figueredo de Santana, Ana Fucs et al. · 2023Predicts when explainability (XAI) is needed.
- 05Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and ValuesAlex Kale · 2022ML interpretability reveals undesirable patterns models exploit.
- 06Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI SystemsMahsan Nourani · 2021XAI to bring transparency and improve mental models.
- 07Visual, textual or hybrid: the effect of user expertise on different explanationsMaxwell Szymanski · 2021Compares visual, textual, and hybrid AI explanations.
- 08How Do Visual Explanations Foster End Users' Appropriate Trust in Machine Learning?Fumeng Yang · 2020Example-based visual explanations for an ML classifier.
- 09Questioning the AI: Informing Design Practices for Explainable AI User ExperincesLiao · 2020Informs design practices for explainable-AI user experiences.
- 10Explanation in artificial intelligence: Insights from the social sciencesTim Miller · 2018Draws on social-science insights to inform explainable AI.
- 11A systematic review and taxonomy of explanations in decision support and recommender systemsIngrid Nunes · 2017Builds a taxonomy of explanation approaches.