| Competency (Sciences) | Assignment/Experience |
| Students will be able to effectively communicate scientific information in an appropriate format |
Students will be expected to produce written response papers to articles and videos on AI ethics, as well as on their visits to local technology groups such as Code for Nashville or the Nashville Data Science Meetup. They will give oral presentations on topics, papers, tool demos, and their class projects involving modeling and prediction systems for their application domain of choice.
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Students will display an ability to make observations and collect, analyze, and interpret data to test hypotheses
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Students will be able to explain the means by which datasets are created for machine learning applications, and use machine learning statistical techniques to make inferences from a training dataset, and compare those against a separate testing dataset, in order to measure a model's generalization ability. Metrics such as accuracy, false positive rates, Receiver Operating Characteristic (ROC) curves, and others will be used for these tests and interpretations
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Students will demonstrate knowledge of relevant scientific concepts
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Students will demonstrate an understanding of regression, classification and clustering by developing computer programs which perform these tasks, by performing experiments and answering questions on tests. They will learn "hands on" about over-fitting, regularization and generalization as they see how these affect the performance of their models. They will be required to turn in assignments showing how varying the parameters can affect these factors for or beneficial or detrimental effects.
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Students will be able to evaluate the impact of scientific discoveries on society
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*Because* this will be a Gen Ed course with minimal prerequisites (e.g. no programming prereq), a significant portion of this course will be discussions, readings, and response papers on the subject of impact on society. Students will engage in discussions of the implications of algorithmic decision making, informed by their exposure to readings and videos from leading AI-ethics researchers on topics of bias, fairness transparency and accountability.
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