Responsible Data Science
DS-UA 202 and DS-GA 1017
DS-UA 202 and DS-GA 1017
DS-UA 202: Elisha Cohen, Data Science Faculty Fellow, Center for Data Science, NYU
DS-GA 1017: Julia Stoyanovich, Associate Professor of Data Science and of Computer Science & Engineering, NYU
The first wave of data science focused on accuracy and efficiency – on what we can do with data. The second wave focuses on responsibility – on what we should and shouldn’t do. Irresponsible use of data science can cause harm on an unprecedented scale. Algorithmic changes in search engines can sway elections and incite violence; irreproducible results can influence global economic policy; models based on biased data can legitimize and amplify racist policies in the criminal justice system; algorithmic hiring practices can silently and scalably violate equal opportunity laws, exposing companies to lawsuits and reinforcing the feedback loops that lead to lack of diversity. Therefore, as we develop and deploy data science methods, we are compelled to think about the effects these methods have on individuals, population groups, and on society at large.
Responsible Data Science is a technical course that tackles the issues of ethics, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection.
The course is developed by Julia Stoyanovich, and taught by her and Elisha Cohen.
DS-UA 202: Introduction to Data Science, Introduction to Computer Science, or similar courses
DS-GA 1017: Introduction to Data Science, Introduction to Computer Science, or similar courses
See Brightspace.