Proceedings of the Conference on Fairness, Accountability, and Transparency

2019

39 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety Research, AI Ethics, Fairness & Society

Citations in the corpus

• The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies (2020, background): “Although post-hoc explanations can be misleading, a potential solution could be to develop post-hoc explanation methods that include argumentative support for their claims [70].”
• Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases (2021, background): “We consider our work as part of ongoing critical research on responsible AI (Elish et al., 2021) that aims to provide explainable and fair systems to support human experts.”