DAISY is a structured form tool that generates more complete AI disclosure statements for research papers without reducing author comfort levels.
InProceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20)
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 4roles
background 2polarities
support 2representative citing papers
Interval counterfactual explanations outperform point counterfactuals and feature importance scores in boosting model understanding and demonstrated trust according to a within-subjects user study.
Ethnographic study of feminist civic-tech data work argues reparative AI dataset production requires resetting accountability ties to center those harmed by current practices.
citing papers explorer
-
AI Disclosure with DAISY
DAISY is a structured form tool that generates more complete AI disclosure statements for research papers without reducing author comfort levels.
-
Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
Interval counterfactual explanations outperform point counterfactuals and feature importance scores in boosting model understanding and demonstrated trust according to a within-subjects user study.
-
Can Data Work be Reparative?
Ethnographic study of feminist civic-tech data work argues reparative AI dataset production requires resetting accountability ties to center those harmed by current practices.
- Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions