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Background: This study aims to evaluate the Chicago Teen Pregnancy PreventionInitiative delivery optimization outcomes given policy-neutral andpolicy-focused approaches to deliver this program to at-risk teens across theCity of Chicago. Methods: We collect and compile several datasets from publicsources including: Chicago Department of Public Health clinic locations, twopublic health statistics datasets, census data of Chicago, list of Chicagopublic high schools, and their Locations. Our policy-neutral approach willconsist of an equal distribution of funds and resources to schools and centers,regardless of past trends and outcomes. The policy-focused approaches willevaluate two models: first, a funding model based on prediction models fromhistorical data; and second, a funding model based on economic and socialoutcomes for communities. Results: Results of this study confirms our initialhypothesis, that even though the models are optimized from a machine learningperspective, there is still possible that the models will produce wildlydifferent results in the real-world application. Conclusions: When ethics andethical considerations are extended beyond algorithmic optimization toencompass output and societal optimization, the foundation and philosophicalgrounding of the decision-making process become even more critical in theknowledge discovery process.

Ethics, Data Science, and Health and Human Services: Embedded Bias in Policy Approaches to Teen Pregnancy Prevention