6 citations · 20 across the 12 of their papers we have counts for
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cs.LG2022★ 2 cited
Robustness and Usefulness in AI Explanation Methods
Erick Galinkin
Explainability in machine learning has become incredibly important as machine learning-powered systems become ubiquitous and both regulation and public sentiment begin to demand an…
cs.AI2022★ 3 cited
Towards a Responsible AI Development Lifecycle: Lessons From Information Security
Erick Galinkin
Legislation and public sentiment throughout the world have promoted fairness metrics, explainability, and interpretability as prescriptions for the responsible development of ethic…