Defense Advanced Research Projects Agency (DARPA) Agent Markup Language Computer Aided Knowledge Acquisition

By L. Lacy. 2005

386 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Gabrielle Ras
2. Marcel A. J. van Gerven
3. Shalmali Joshi
4. Willem F. G. Haselager
5. Lianwei Wu
6. Y. Rao
7. William Yang Wang
8. Hao Liang

Citations in the corpus

• Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges (2018, background): “…by official regulations such as the General Data Protection Regulation (GDPR), also mentioned in (Doshi-Velez and Kim, 2017; Holzinger et al., 2017a), incentives to promote the field of explain-ability (Gunning, 2017) and institutional initiatives to ensure the safe development of AI such as OpenAI.”
• Towards Explainable NLP: A Generative Explanation Framework for Text Classification (2018, background): “Our work is closely aligned with Explainable Artificial Intelligence (XAI) (Gunning, 2017), which is claimed to be essential if users are to understand, appropriately trust, and effectively manage this incoming generation of artificially intelligent partners.”
• A Survey on the Explainability of Supervised Machine Learning (2020, unknown): “In May 2017, the DARPA launched the program XAI (Gunning, 2017) which aims at providing explainable and highly accurate models.”
• What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use (2019, background): “This has led to the development of “interpretable” or “explainable” machine learning models (Gunning, 2017) as a measure to overcome barriers of trust and adoption.”
• DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020, background): “However, the current deep learning models belong to black box models, which are dif-ficult to achieve intrinsic explainability (Gunning, 2017).”