Transparency
How clearly a system conveys what it does and why, so users can understand it.
25 papers carry this theme
In these papers
- 01“I know even if you don’t tell me”: Understanding Users’ Privacy Preferences Regarding AI-based Inferences of Sensitive Information for PersonalizationSumit Asthan · 2024Studies how transparency of inferences affects users.
- 02A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?Ziming Wang · 2024Stresses explaining to the actual end-user, not just developers.
- 03User Characteristics in Explainable AI: The Rabbit Hole of Personalization?Robert Nimmo · 2024XAI for transparency and trust among users.
- 04Predicting the Need for XAI from High-Granularity Interaction DataVagner Figueredo de Santana, Ana Fucs et al. · 2023Targets making AI more understandable to humans.
- 05Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data WorkJen Rogers · 2023Makes visible what was done, by whom or what.
- 06Explaining User Models with Different Levels of Detail for Transparent Recommendation: A User StudyMouadh Guesmi · 2022Interactive, layered explanations of the user model.
- 07Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI SystemsMahsan Nourani · 2021Aims to improve users' understanding of AI models.
- 08Interactive Music Genre Exploration with Visualization and Mood ControlYu Liang · 2021Visualization to improve understandability of novel items.
- 09On-demand Personalized Explanation for Transparent RecommendationMouadh Guesmi · 2021On-demand explanations for transparent recommendation.
- 10Visual, textual or hybrid: the effect of user expertise on different explanationsMaxwell Szymanski · 2021Explanations for AI transparency and accountability.
- 11Exploring Mental Models for Transparent and Controllable Recommender Systems: A Qualitative StudyThao Ngo · 2020The rationale behind personalization typically stays opaque.
- 12How Do Visual Explanations Foster End Users' Appropriate Trust in Machine Learning?Fumeng Yang · 2020Explores spatial layout and visual representation of explanations.
- 13Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information?Arron Springer, Steve Whittaker · 2020Studies when/why/how users want algorithmic transparency info.
- 14Questioning the AI: Informing Design Practices for Explainable AI User ExperincesLiao · 2020Designs explainability features into AI systems.
- 15Personalized Explanations for Hybrid Recommender SystemsPigi Kouki · 2019Growing need for transparency as recommenders grow complex.
- 16To Explain or not to Explain: the Effects of Personal Characteristics when Explaining Music RecommendationsMartijn Millecamp · 2019Counters the recommender 'black box' with explanations.
- 17“This App Would Like to Use Your Current Location to Better Serve You”: Importance of User Assent and System Transparency in Personalized Mobile ServicesTsai-Wei Chen · 2018Studies system transparency and user assent for personalization.
- 18Explanation in artificial intelligence: Insights from the social sciencesTim Miller · 2018Argues AI explanation should mirror how humans explain to each other.
- 19Explanations as Mechanisms for Supporting Algorithmic TransparencyEmilee Rader · 2018Explanations empower informed choices about algorithmic systems.
- 20Moodplay: Interactive Music Recommendation Based on Artists' Mood SimilarityIvana Andjelkovic, Denis Parra et al. · 2018Centers explanation, transparency, and control beyond accuracy.
- 21Towards Algorithmic Experience: Initial Efforts for Social Media ContextsOscar Alvarado · 2018Proposes 'Algorithmic Experience' as analytical lens on interacting with algorithms.
- 22“Algorithms ruin everything”: #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social MediaDarren Gergle · 2017Users build folk theories of opaque algorithmic curation.
- 23User Preferences for Hybrid ExplanationsPigi Kouki · 2017Studies which explanation formats users prefer.
- 24Crowd-Based Personalized Natural Language Explanations for RecommendationsShuo Chang · 2016Explanations help users decide whether to take recommendations.
- 25How Much Information? Effects of Transparency on Trust in an Algorithmic InterfaceRene Kizilcec · 2016Studies how transparent design of algorithmic interfaces builds awareness.