DS-GA 3001.009: Special Topics in Data Science:
Responsible Data Science

New York University, Center for Data Science, Spring 2019

Lecture: Mondays from 11am-12:40pm; Lab: Thursdays from 5:20pm-6:10pm

Location: 60 5th Avenue, Room 110

Instructor: Julia Stoyanovich, Assistant Professor of Data Science, Computer Science and Engineering.
Office hours Mondays 1:30-3pm or by appointment, at 60 5th Avenue, Room 605.

Section Leader: Udita Gupta. Office hours Thursdays 4-5pm at 60 5th Avenue, Room 663.

Syllabus: pdf

Course Description:

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 and taught by Julia Stoyanovich, Assistant Professor at the Center for Data Science and at the Tandon School of Engineering, and member of the NYC Automated Decision Systems Task Force.

Prerequisites: Introduction to Data Science, Introduction to Computer Science, or similar courses.

Background Reading (required)

Background Reading (optional)


This weekly schedule is tentative and is subject to change.

Date Topic Materials Assignments
Jan 28 Lecture: Introduction and background
Topics: Course outline, aspects of responsibility in data science through recent examples.
“Bias in Computer Systems”, Friedman and Nissenbaum (1996) ACM DL
“Machine Bias”, Angwin, Larson, Mattu, Kirchner (2016) ProPublica
“Data, Responsibly”, Abiteboul and Stoyanovich (2015) ACM SIGMOD blog
Jan 31 Lab: ProPublica’s Machine Bias jupyter notebook  
Feb 4 Lecture: Fairness
Topics: A taxonomy of fairness definitions; individual and group fairness. The importance of a socio-technical perspective: stakeholders and trade-offs.
“Big Data’s Disparate Impact”, Barocas and Selbst (2016) pdf
“Fairness through awareness”, Dwork, Hardt, Pitassi, Reingold, Zemel (2012) ACM DL
“On the (im)possibility of fairness”, Friedler, Scheidegger, Venkatasubramanian (2016) arXiv
Feb 7 Lab: IBM’s AI Fairness 360 toolkit
“Data preprocessing techniques for classification without discrimination”, Kamiran and Calders (2012) pdf
jupyter notebook
Feb 11 Lecture: Fairness
Topics: Impossibility results; causal definitions; fairness beyond classification.
“Fair prediction with disparate impact: A study of bias in recidivism prediction instruments”, Chouldechova (2017) arXiv
“Inherent Trade-Offs in the Fair Determination of Risk Scores”, Kleinberg, Mullainathan, Raghavan (2017) pdf
“Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions”, Mitchell, Porash, Barocas (2018) arXiv
Feb 14 Lab: IBM’s AI Fairness 360 toolkit
“Certifying and removing disparate impact”, M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian (2015) pdf
jupyter notebook
HW1 assigned
Feb 18 No class, university holiday    
Feb 21 Lab: Fairness and causality slides  
Feb 25 Lecture: Anonymity and privacy, guest lecture by Daniela Hochfellner
Topics: Overview of responsible data sharing. Anonymization techniques; the limits of anonymization. Harms beyond re-identification.
“The Belmont Report” (1979) pdf
“Critical questions for Big Data”, danah boyd and Cate Crawford (2012) pdf
slides HW1 due
Feb 28 Lab: Anonymity and privacy jupyter notebook jupyter notebook brute force slides  
Mar 4 Lecture: no class, snow day    
Mar 7 Lab: Anonymity and privacy (see Mar 11 materials)    
Mar 11 Lecture: Anonymity and privacy
Topics: Differential privacy; privacy-preserving synthetic data generation; exploring the privacy / utility trade-off.
“A firm foundation for private data analysis”, C. Dwork (2011) ACM DL
“Can a set of equations keep U.S. census data private?”, J. Mervis (2019) Science
Mar 14 Lab: Data Synthesizer
“DataSynthesizer: Privacy-Preserving Synthetic Datasets”, Ping, Stoyanovich, Howe (2017) ACM DL
jupyter notebook
HW2 assigned
Mar 18 No class, university holiday    
Mar 21 No class, university holiday    
Mar 25 Lecture: Profiling and particularity, guest lecture by Solon Barocas
Topics: Profiling and particularity
“On individual risk”, Dawid (2017) pdf
“We Are All Different: Statistical Discrimination and the Right to Be Treated as an Individual”, Lippert-Rasmussen (2011) pdf
Mar 28 Lab: Data profiling jupyter notebook
HW2 due
Apr 1 Lecture: Data profiling
Topics: Overview of the data science lifecycle. Data profiling and validation.
“Profiling relational data: a survey”, Abedjan, Golab, Naumann (2015) pdf
“To predicts and serve?”, Lum and Isaac (2016) pdf
slides HW3 assigned
Apr 4 Lab: Data profiling    
Apr 8 Lecture: Transparency
Topics: Auditing black-box models; explainable machine learning.
“Why should I trust you? Explaining the predictions of any classifier”, Ribeiro, Singh, Guestrin (2016) pdf
“Algorithmic transparency via quantiative input influence: theory and experiments with learning systems”, Datta, Sen, Zick (2016) pdf
Apr 11 Lab: LIME jupyter notebook HW3 due
HW4 assigned
Apr 15 Lecture: Transparency
Topics: Discrimination in online ad delivery. Interpretability.
“Automated Experiments on Ad Privacy Settings”, Datta, Tschantz, Datta (2015) pdf
“Discrimination through optimization: How Facebook’s ad delivery can lead to skewed outcomes”, Ali, Sapiezynski, Bogen, Korolova, Mislove, Rieke (2019) pdf
“Facebook has been charged with housing discrimination by the US government”, Russell Brandom for The Verge, Mar 28, 2019 read online
Apr 18 Lab: Final review    
Apr 22 Lecture: Final exam (in class)    
Apr 25 Lab: Nutritional labels jupyter notebook
HW4 due Project assigned
Apr 29 Lecture: Data Cleaning guest lecture by Sebastian Schelter
Topics: Overview of data cleaning.
Reading: “Quantitative Data Cleaning for Large Databases”, Joe Hellerstein (2008) pdf
May 2 Lab: Data cleaning jupyter notebook  
May 6 Lecture: Legal frameworks, codes of ethics, and personal responsibility.
Reading: “The Belmont Report” (1979) pdf
“The Menlo Report” (2012) pdf
“Chapter 6: Ethics. Bit by Bit: Social Research in the Digital Age”, Matthew Salganik (2017) online
May 9 Lab: Talk by Rashida Richardson
Topics: Civil rights, predictive policing, and criminal justice
Reading: “Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice”, Richardson, Schultz, Crawford (2019) online
CDS 7th Floor open area, 4-5:30pm
May 13 Lecture: Project presentations   Project report due