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Found 8 papers, 2 papers with code
EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations
Explanations in interactive machine-learning systems facilitate debugging and improving prediction models.
Exploring the Impact of Lay User Feedback for Improving AI Fairness
Fairness in AI is a growing concern for high-stakes decision making.
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platform
Our research involved a comprehensive examination of the impact of global explanations rooted in both data-centric and model-centric perspectives within systems designed to support healthcare experts in optimising machine learning models through both automated and manual data configurations.
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence.
Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness
In order to create reliable, safe and trustworthy systems through human-centred artificial intelligence (HCAI) design, recent efforts have produced user interfaces (UIs) for AI experts to investigate the fairness of AI models.
Towards Involving End-users in Interactive Human-in-the-loop AI Fairness
We evaluated the use of this prototype system through an online study.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
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ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
To close this gap, we present the ORBIT dataset and benchmark, grounded in the real-world application of teachable object recognizers for people who are blind/low-vision.
