Abigail Santiago
ABSTRACT
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- Google. 2019. Fairness: Types of bias | machine learning crash course | google developersn. https://developers.google.com/machine-learning/crash-course/fairness/types-of-biasGoogle Scholar
- A. Kube, S. Das, and P. Fowler. 2019. Allocating Interventions Based on Predicted Outcomes:A Case Study on Homelessness Services.. In Proceedings of the AAAI Conference on Artificial Intelligence. 622–629.Google Scholar
- F. Mehrabi, N.and Morstatter, N. Saxena, K. Lerman, and A. Galstyan. 2021. A Survey on Bias and Fairness in Machine Learning. Comput. Surveys 54, 6 (2021), 1–35.Google ScholarDigital Library
- H. Nisar, Horseman C. Vachon, M., and J. Murdoch. [n.d.]. Market Predictors of Homelessness: How Housing and Community Factors Shape Homelessness Rates Within Continuums of Care.([n. d.]).Google Scholar
- B. VanBerlo, M. Ross, Rivard, J., and R. Booker. 2021. Interpretable machine learning approaches to prediction of chronic homelessness.Engineering Applications of Artificial Intelligence 102 (2021), 104–243.Google ScholarCross Ref
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