Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016)
By Been Kim, Dmitry M. Malioutov, Kush R. Varshney. 2016
19 citations recorded by the source corpus.
This is the Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), which was held in New York, NY, June 23, 2016. Invited speakers were Susan Athey, Rich Caruana, Jacob Feldman, Percy Liang, and Hanna Wallach.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Jo Vermeulen
- Mohan Kankanhalli
- Brian Y. Lim
- Ashraf Abdul
- Kohei Hayashi
- Danding Wang
- Satoshi Hara
- Atoosa Kasirzadeh
Citations in the corpus
- Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach (2016, methodology): “Interpretability of complex machine learning models are now in high demand [10, 11].”
- Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, background): “Fairness, Accountability, and Transparency (FAT-ML) workshop at KDD 2014-2017 [9], ICML 2016 Workshop on Human Interpretability in Machine Learning (WHI) at [93], NIPS 2016 Interpretable ML for Complex Systems [181], IJCAI 2017 Workshop on Ex-plainable AI [1].”