ML in Gov resources

Resource List

Computer Vision + Visual Census

Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences, 114(50), 13108-13113.

Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and google street view to estimate the demographic makeup of the us. arXiv preprint arXiv:1702.06683. (arxiv version of below paper)

Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., & Fei-Fei, L. (2015). Visual census: Using cars to study people and society. Bigvision.

Free-text to automatically assign codes (aka autocoding) Within gov

(BLS) Measure, Alex (2017). Deep neural networks for worker injury autocoding.

(BLS) Measure, Alex (2014). Automatic coding of worker injury narratives. Basis for 1.

(Census) Dumbacher, Brian; Hanna, Demetria (2017). Passive Data Collection, System-to-system data collection, machine learning to improve economic surveys.

Outside gov

Skinner, Michael (2018). Product categorization with LSTMs and balanced pooling views.

Also check the other "data challenge papers" here: https://sigir-ecom.github.io/accepted-papers.html

Ding, Liya et al. (2015). Auto-Categorization of HS Code Using Background Net Approach.

Amazon Mechanical Turk / Active Learning for better training data

Within Gov

Pierce, Cynthia et al. (2013). Crowd Sourcing data through Amazon Mechanical Turk.

Outside Gov

Settles, Burr (2010). Active Learning.

MTurk for Survey Pretesting (BLS) Yu, Erica et al., 2015: https://www.bls.gov/osmr/pdf/st150260.pdf (NCI), Fowler, Stephanie et al., 2015: https://s3.amazonaws.com/sitesusa/wp-content/uploads/sites/242/2016/03/C2_Fowler_2015FCSM.pdf (NCSES), Morrison, Rebecca et al., 2017:- https://www.census.gov/fedcasic/fc2018/ppt/5AMorrison.pdf (DOE), Greenblatt, Jeffery et al., 2013:- https://www.osti.gov/biblio/1171618

Alternative Data Sources + Web Scraping (ORNL) Wang, Chieh (Ross) et al. : Web Scraping rail-fan photos to track crude oil shipments. http://onlinepubs.trb.org/onlinepubs/Conferences/2019/FreightData/CrudebyRailRoutesWang.pdf (Census) Dumbacher, Brian et al. : Scraping Assisted By Learning. https://www.census.gov/content/dam/Census/newsroom/press-kits/2018/jsm/jsm-presentation-web-scraping.pdf

ML fairness/algorithmic bias (not govt specific):

UK House of Commons Science and Technology Committee: “Algorithms in Decision Making”

ACM Conference on Fairness, Accountability, and Transparency (“FATML”)

Friedler et al. (2016). “On the (im-)possibility of fairness”

Corbett-Davies et al. (2017). “Algorithmic decision making and the cost of fairness”

IMPACTnet: aicommons.com