Concern about the harmful effects of machine learning algorithms and big data AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics. News stories appear regularly about credit algorithms that discriminate against women, medical algorithms that discriminate against African Americans, hiring algorithms that base decisions on gender, and more. In most cases, the data scientists who developed and deployed these decision making algorithms and data processes had no such intentions, and were unaware of the harmful impact of their work.
This data science ethics course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models, do better data analysis and avoid these problems.Read more.
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The techniques and tools covered in Applied Data Science Ethics are most similar to the requirements found in Data Scientist job advertisements.
