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Sarah Tan
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“No fracking way!” Documentary film, discursive opportunity, and local opposition against hydraulic fracturing in the United States, 2010 to 2013
IB Vasi, ET Walker, JS Johnson, HF Tan
American Sociological Review 80 (5), 934-959, 2015
2272015
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation
S Tan, R Caruana, G Hooker, Y Lou
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018
158*2018
Considerations When Learning Additive Explanations for Black-Box Models
S Tan, G Hooker, P Koch, A Gordo, R Caruana
arXiv preprint arXiv:1801.08640 3, 2018
108*2018
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations
Y Zhang, K Song, Y Sun, S Tan, M Udell
ICML 2019 AI for Social Good Workshop, 2019
56*2019
Tree space prototypes: Another look at making tree ensembles interpretable
S Tan, M Soloviev, G Hooker, MT Wells
Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference, 23-34, 2020
552020
Investigating Human+ Machine Complementarity: A Case Study on Recidivism
S Tan, J Adebayo, K Inkpen, E Kamar
arXiv preprint arXiv:1808.09123, 2018
33*2018
Axiomatic Interpretability for Multiclass Additive Models
X Zhang, S Tan, P Koch, Y Lou, U Chajewska, R Caruana
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
26*2019
How Interpretable and Trustworthy are GAMs?
CH Chang, S Tan, B Lengerich, A Goldenberg, R Caruana
Proceedings of the 27th ACM SIGKDD International Conference on Knowledge …, 2021
252021
Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism
K Mallari, K Inkpen, P Johns, S Tan, D Ramesh, E Kamar
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems …, 2020
112020
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
B Lengerich, S Tan, CH Chang, G Hooker, R Caruana
International Conference on Artificial Intelligence and Statistics, 2402-2412, 2020
102020
A Bayesian Evidence Synthesis Approach to Estimate Disease Prevalence in Hard-To-Reach Populations: Hepatitis C in New York City
S Tan, S Makela, D Heller, K Konty, S Balter, T Zheng, JH Stark
Epidemics 23 (June 2018), 96-109, 2018
82018
Interpretable Approaches to Detect Bias in Black-Box Models
S Tan
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society …, 2018
52018
A Double Parametric Bootstrap Test for Topic Models
S Seto, S Tan, G Hooker, MT Wells
NeurIPS 2017 Interpretability Symposium, 2017
12017
Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies
H Wu, S Tan, W Li, M Garrard, A Obeng, D Dimmery, S Singh, H Wang, ...
arXiv preprint arXiv:2111.03267, 2021
2021
Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data
Z Chen, S Tan, H Nori, K Inkpen, Y Lou, R Caruana
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021
2021
Probabilistic Matching: Incorporating Uncertainty to Correct for Selection Bias
HF Tan, GJ Hooker, MT Wells
NeurIPS 2016 Causal Inference Workshop, 2016
2016
Two Ways of Modeling Hospital Readmissions: Mixed and Marginal Models
HF Tan, R Low, S Ito, R Gregory, L Bielory, V Dunn
Proceedings of the Joint Statistical Meetings, 2013
2013
Using PROC GENMOD to Investigate Drug Interactions: Beta Blockers and Beta Agonists and Their Association with Hospital Admissions
HF Tan, R Low, S Ito, R Gregory, V Dunn
Proceedings of SAS Global Forum, 2013
2013
Hospital Readmission Rates: Related To Ed Volume, Population, And Economic Variables
RB Low, S Ito, R Gregory, L Rassi, HF Tan, C Jacobs
Academic Emergency Medicine 19 (4), S208-S209, 2012
2012
Friends Don’t Let Friends Deploy Black-Box Models: Detecting and Preventing Bias via Transparent Modeling
R Caruana, Y Lou, S Tan, J Gehrke, P Koch, M Sturm, N Elhadad, ...
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Artikelen 1–20