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1-Introduction/02-ethics/README.md

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 The latter requires [collaborative approaches to defining ethics cultures](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) that build emotional connections and consistent shared values _across organizations_ in the industry. This calls for more [formalized data ethics cultures](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) in organizations - allowing _anyone_ to [pull the Andon cord](https://en.wikipedia.org/wiki/Andon_(manufacturing)) (to raise ethics concerns early in the process) and making _ethical assessments_ (e.g., in hiring) a core criteria team formation in AI projects.
 
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 ## [Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/3) 🎯
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 ## Review & Self Study 
 
 Courses and books help with understanding core ethics concepts and challenges, while case studies and tools help with applied ethics practices in real-world contexts. Here are a few resources to start with.
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 * [Ethics and Data Science](https://resources.oreilly.com/examples/0636920203964) - O'Reilly EBook (M. Loukides, H. Mason et. al)
 * [Data Science Ethics](https://www.coursera.org/learn/data-science-ethics#syllabus) - online course from the University of Michigan.
 * [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - case studies from the University of Texas.
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 # Assignment 
 
 [Write A Data Ethics Case Study](assignment.md)

2-Working-With-Data/06-non-relational/README.md

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 The fields of interest in this document are: `created_at`, `id`, and `text`.
 
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 ## 🚀 Challenge