All Stories

  1. XCrowd: Combining Explainability and Crowdsourcing to Diagnose Models in Relation Extraction
  2. Adaptive In-Context Learning with Large Language Models for Bundle Generation
  3. A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities
  4. Robust Link Prediction over Noisy Hyper-Relational Knowledge Graphs via Active Learning
  5. ``It Is a Moving Process": Understanding the Evolution of Explainability Needs of Clinicians in Pulmonary Medicine
  6. Editorial: Special Issue on Human in the Loop Data Curation
  7. Revisiting Bundle Recommendation for Intent-aware Product Bundling
  8. The First Workshop on Personalized Generative AI @ CIKM 2023: Personalization Meets Large Language Models
  9. How do you feel? Measuring User-Perceived Value for Rejecting Machine Decisions in Hate Speech Detection
  10. “☑ Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms
  11. HybridEval: A Human-AI Collaborative Approach for Evaluating Design Ideas at Scale
  12. Perspective: Leveraging Human Understanding for Identifying and Characterizing Image Atypicality
  13. Revisiting Bundle Recommendation
  14. Ready Player One! Eliciting Diverse Knowledge Using A Configurable Game
  15. What Should You Know? A Human-In-the-Loop Approach to Unknown Unknowns Characterization in Image Recognition
  16. Through-Screen Visible Light Sensing Empowered by Embedded Deep Learning
  17. Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations
  18. What do You Mean? Interpreting Image Classification with Crowdsourced Concept Extraction and Analysis
  19. Peer Grading the Peer Reviews: A Dual-Role Approach for Lightening the Scholarly Paper Review Process
  20. Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison
  21. Leveraging Crowdsourcing Data for Deep Active Learning An Application
  22. Interacting Attention-gated Recurrent Networks for Recommendation