Bibliography and References

Foundational Texts¶

• Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MIT Press. Available at fairmlbook.org.

• Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153-163.

• Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3-4), 211-407.

• Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016). Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807.

Federal Standards and Frameworks¶

• Executive Office of the President. (2025). Executive Order 14179: Removing Barriers to American Leadership in Artificial Intelligence. https://www.whitehouse.gov/presidential-actions/2025/01/removing-barriers-to-american-leadership-in-artificial-intelligence/

• Executive Office of the President. (2025). Executive Order 14275: Restoring Common Sense to Federal Procurement. https://www.whitehouse.gov/presidential-actions/2025/04/restoring-common-sense-to-federal-procurement/

• Federal Committee on Statistical Methodology. Statistical Policy Working Papers. Available at fcsm.gov.

• Federal Committee on Statistical Methodology. (2025). FCSM 25-03: AI-Ready Federal Statistical Data: An Extension of Communicating Data Quality. https://statspolicy.gov/assets/fcsm/files/docs/FCSM.25.03_AI-Ready-Extension-Data-Quality.pdf

• NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1. https://csrc.nist.gov/pubs/ai/100/1/final

• NIST. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. NIST AI 600-1. https://csrc.nist.gov/pubs/ai/600/1/final

• Office of Management and Budget. (2024). Memorandum M-24-10: Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence. (Rescinded April 2025; replaced by M-25-21.) https://www.whitehouse.gov/wp-content/uploads/2024/03/M-24-10-Advancing-Governance-Innovation-and-Risk-Management-for-Agency-Use-of-Artificial-Intelligence.pdf

• Office of Management and Budget. (2025). Memorandum M-25-21: Accelerating Federal Use of AI through Innovation, Governance, and Public Trust. https://www.whitehouse.gov/omb/information-resources/guidance/memoranda/

• Office of Management and Budget. (2025). Memorandum M-25-22: Driving Efficient Acquisition of Artificial Intelligence in Government. https://www.whitehouse.gov/omb/information-resources/guidance/memoranda/

• Office of Management and Budget. Statistical Policy Directive No. 15: Race and Ethnic Standards for Federal Statistics and Administrative Reporting.

Survey Methodology¶

• Andridge, R. R., & Little, R. J. A. (2010). A review of hot deck imputation for survey non-response. International Statistical Review, 78(1), 40–64.

• Groves, R. M., & Couper, M. P. (1998). Nonresponse in Household Interview Surveys. Wiley.

• Kish, L. (1965). Survey Sampling. Wiley.

• Little, R. J. A., & Rubin, D. B. (2019). Statistical Analysis with Missing Data (3rd ed.). Wiley.

• Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.

Record Linkage and Data Integration¶

• Newcombe, H. B., Kennedy, J. M., Axford, S. J., & James, A. P. (1959). Automatic linkage of vital records. Science, 130, 954–959.

• Fellegi, I. P., & Sunter, A. B. (1969). A theory for record linkage. Journal of the American Statistical Association, 64(328), 1183–1210.

• Christen, P. (2012). Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer.

• Steorts, R. C., Ventura, S. L., Sadinle, M., & Fienberg, S. E. (2014). A comparison of blocking methods for record linkage. In Proceedings of the 2014 IEEE International Conference on Data Mining Workshop (ICDMW) (pp. 1–8). IEEE.

Dimension Reduction and Unsupervised Learning¶

• Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417–441.

• van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.

• McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426.

AI Agents and Agentic Systems¶

• Microsoft AI Red Team. (2025). “Lessons from Red Teaming 100 Generative AI Products.” arXiv:2501.07238. https://arxiv.org/abs/2501.07238

• OpenAI. (2025). “A Practical Guide to Building Agents.” https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf

• Anthropic. (2024). “Building Effective AI Agents.” https://resources.anthropic.com/building-effective-ai-agents

• Google. (2024). “Agents” (whitepaper). https://cloud.google.com/transform/agents-paper (URL as of 2024; location may shift)

Census and Survey Methodology¶

• Abowd, J. M. (2018). The U.S. Census Bureau adopts differential privacy. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

• CDC/NIOSH. (2022). 100 million and counting! NIOSH Science Blog. https://www.cdc.gov/niosh/bulletin/2022/100-million-and-counting.html

• Abowd, J. M., Benedetto, G., & Stinson, M. (2006). The Creation and Use of the SIPP Synthetic Beta (Technical report). U.S. Census Bureau.

• Garfinkel, S. L. (2015). De-identification of personal information. NIST Internal Report 8053.

• Raghunathan, T. E., Reiter, J. P., & Rubin, D. B. (2003). Multiple imputation for statistical disclosure limitation. Journal of Official Statistics, 19(1), 1–16.

• U.S. Census Bureau. (2021). Key parameters set to protect privacy in 2020 Census results (press release, June 8, 2021). https://www.census.gov/newsroom/press-releases/2021/2020-census-key-parameters.html

• U.S. Census Bureau. (2022). 2020 Census Post-Enumeration Survey Results. Coverage Measurement Program.

AI and Critical Thinking¶

• Hendrick, C. (2025). Ultra-processed minds: The end of deep reading and what it costs us. The Learning Dispatch.

Model Documentation¶

• Mitchell, M., et al. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency.

• Gebru, T., et al. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92.

Research Design and Validity¶

• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference (1st ed.). Houghton Mifflin.

Statistical Methods¶

• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.

• Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.

Machine Learning Methods¶

• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

• Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.

• Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems, 35.

• Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth.

• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. Available at https://hastie.su.domains/ElemStatLearn/

• Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67.

• Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

• Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.

• Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.

• Stekhoven, D. J., & Bühlmann, P. (2012). MissForest: Non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112–118.

• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.

Neural Networks and Deep Learning¶

• Ba, J. L., Kiros, J. R., & Hinton, G. E. (2016). Layer normalization. arXiv preprint arXiv:1607.06450.

• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) (pp. 770–778).

• Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR). arXiv:1412.6980.

• Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408.

• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.

Explainability and Interpretability¶

• Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.

• Shapley, L. S. (1953). A value for n-person games. In H. W. Kuhn & A. W. Tucker (Eds.), Contributions to the Theory of Games II (pp. 307–317). Princeton University Press.

Natural Language Processing and Transformers¶

• Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

• Devlin, J., et al. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019.

• Sennrich, R., Haddow, B., & Birch, A. (2016). Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1715–1725). Association for Computational Linguistics.

Survey Text Coding and NLP¶

• NIOSH. (2024). NIOSH Industry and Occupation Computerized Coding System (NIOCCS). U.S. Department of Health and Human Services, CDC/NIOSH. https://csams.cdc.gov/nioccs/About.aspx

• Friesen, M.C., et al. (2022). Beware the Grizzlyman: A comparison of job- and industry-based noise exposure estimates using manual coding and the NIOSH NIOCCS machine learning algorithm. Annals of Work Exposures and Health, 66(7), 903-914. https://pubmed.ncbi.nlm.nih.gov/35537195/

• Coutinho, I., & Martins, B. (2024). ICD coding of death certificates with generative language models. PLOS Digital Health, 3(2), e0001245.

• Pedersen, B.R., Bøgh Andersen, E., Rua, A.G., Jensen, K., & Gjøl Christiansen, M. (2023). Coding Historical Causes of Death Data with Large Language Models. In: Bridging the Gap Between AI and Reality (AISoLA 2023). Lecture Notes in Computer Science, vol 14129. Springer.

• NCHS. (2025). Instructions for Classifying the Underlying Cause-of-Death, ICD-10, 2025. CDC. https://www.cdc.gov/nchs/nvss/manuals/2025/2a-2025.html

• Bureau of Labor Statistics. “Occupational Employment and Wage Statistics Technical Notes.” https://www.bls.gov/oes/oes_doc.htm

• McKinney, W. (2020). “Using Machine Learning to Automate Data Coding at the Bureau of Labor Statistics.” Forbes. https://www.forbes.com/sites/cognitiveworld/2020/08/01/using-machine-learning-to-automate-data-coding-at-the-bureau-of-labor-statistics-bls/

• Elias, P., et al. (2007). “Evaluating automated occupation coding.” PMC 3316486. https://pmc.ncbi.nlm.nih.gov/articles/PMC3316486/

• “Occupation Autocoding” (working paper, May 2024). https://www.occautocoder.com/static/paper/occupation_autocoding_May2024.pdf

• Scholastica Journal of Human and Social Sciences. “Utilizing Large Language Models for Text-Based Industry Classification” (2024). https://jhss.scholasticahq.com/article/138087-utilizing-large-language-models-for-text-based-industry-classification.pdf

• Coris AI. “GPT-4 Merchant Industry Classification: MCC, NAICS, Risk Analyst” (2024). https://www.coris.ai/blog/gpt-4-merchant-industry-classification-mcc-naics-risk-analyst

• Batatia, A., et al. (2025). “Text Classification in the LLM Era.” arXiv 2502.11830. https://arxiv.org/html/2502.11830v1

• PMC 3900052. “Inter-coder reliability for occupation coding.” https://pmc.ncbi.nlm.nih.gov/articles/PMC3900052/

• Kessler Scholars. “Feasibility and Reliability of Automated Coding of Occupation in the HRS.” https://kesslerscholars.org/publications/feasibility-and-reliability-of-automated-coding-of-occupation-in-the-health-and-retirement-study/

Prompt Engineering¶

• Maxim AI. “Prompt Versioning: Best Practices for AI Engineering Teams” (2025). https://www.getmaxim.ai/articles/prompt-versioning-best-practices-for-ai-engineering-teams/

• Anthropic. “Prompt Engineering Guide: Classification.” https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview

• OpenAI. “Prompt Engineering Guide.” https://platform.openai.com/docs/guides/prompt-engineering

• Google. “Prompt Design Strategies.” https://ai.google.dev/gemini-api/docs/prompting-strategies

Statistical Disclosure Limitation and Privacy¶

• Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T. B., Song, D., Erlingsson, Ú., Oprea, A., & Raffel, C. (2021). Extracting training data from large language models. Proceedings of the 30th USENIX Security Symposium, 2633–2650.

• Snoke, J., Raab, G. M., Nowok, B., Dibben, C., & Slavković, A. (2018). General and specific utility measures for synthetic data. Journal of the Royal Statistical Society: Series A, 181(3), 663–688.

• National Academies of Sciences, Engineering, and Medicine. (2024). Toward a 21st Century National Data Infrastructure: Mobilizing Information for the Common Good. National Academies Press. https://www.nationalacademies.org/read/27169/chapter/6

• Hogan Lovells. (2024). Model Inversion and Membership Inference: Understanding New AI Security Risks and Mitigating Vulnerabilities. https://www.hoganlovells.com/en/publications/model-inversion-and-membership-inference-understanding-new-ai-security-risks-and-mitigating-vulnerabilities

• Microsoft Research. (2024). The Crossroads of Innovation and Privacy: Private Synthetic Data for Generative AI. https://www.microsoft.com/en-us/research/blog/the-crossroads-of-innovation-and-privacy-private-synthetic-data-for-generative-ai/

• Harvard Data Science Review. Statistical Agencies and AI: Implications for Official Statistics. https://hdsr.mitpress.mit.edu/pub/m3fk4fah

• National Center for Education Statistics / FCSM. Data Protection Toolkit. https://nces.ed.gov/fcsm/dpt/content/1-3-4

• NIST. Statistical Disclosure Limitation. CSRC Glossary. https://csrc.nist.gov/glossary/term/statistical_disclosure_limitation

• U.S. Census Bureau. Federal Statistical Research Data Centers. https://www.census.gov/about/adrm/fsrdc.html

AI Transparency and Governance¶

• Anthropic, Coreweave, et al. (2025). Navigating the Transparency-Privacy Trade-off in AI Systems. arXiv:2601.18127 [URL to be verified]. https://arxiv.org/abs/2601.18127

• Webb, B. (2026). When AI Enters Federal Statistics: A Crosswalk Between Data Quality and AI Trustworthiness Frameworks. Zenodo. https://zenodo.org/records/18772590

Model Selection and Deployment¶

• Bosan, W. (2025). LLM Arena Pareto Frontier: Performance vs Cost. https://winston-bosan.github.io/llm-pareto-frontier/

• Sreenivas, S., et al. (2025). Revisiting Pruning vs Quantization for Small Language Models. Findings of EMNLP 2025. https://aclanthology.org/2025.findings-emnlp.645/

• Inception Labs. (2026). Introducing Mercury 2. https://www.inceptionlabs.ai/blog/introducing-mercury-2

• Tong, Y., et al. (2025). Mercury: A Diffusion-Based Large Language Model Family. arXiv. https://arxiv.org/pdf/2506.17298.pdf

Model Supply Chain Security¶

• NIST. (2022). SP 800-218: Secure Software Development Framework (SSDF) Version 1.1. https://csrc.nist.gov/publications/detail/sp/800-218/final