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Ethics, Fairness, Responsibility, and Privacy in Data Science (DATA 25900) at The University of Chicago

259, Spring 24 edition


Please, check out this schedule frequently as it will likely change a bit throughout the quarter.


#Lecture Date Lecture Keywords Readings Important Dates
1 03/19 Course Overview and Introduction to the Data Science Process Data science lifecycle. Ethics, fairness, responsibility, and privacy issues. Reading 1.1: John P. A. Ioannidis Why Most Published Research Findings Are False PLOS Medicine. 2005 Reading 1.2: Michael Jordan Artificial Intelligence: The Revolution Hasn’t Happened Yet. HDSR 2019. PA0 assigned R1 assigned P assigned
2 03/21 Pitfalls in Inferential Statistics Multiple hypotheses, Bonferroni correction, false discovery rate, statistical vs practical significance    
3 03/26 Data Context and Quality collection, preparation, cleaning, missing data Reading 2.1: Mark D. Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship. Nature Scientific Data. 2016 Reading 2.2: Stephen Stigler. Data Have a Limited Shelf Life. HDSR 2019. R1 due PA0 due R2 assigned PA1 assigned
4 03/28 Causality and Experiments 1/2 causal models, experiments (RCT)    
5 04/02 Causality and Experiments 2/2 causal inference from observational data, human subjects, AB testing, experimental design Reading 3.1: Department of Health, Education, and Welfare. The Belmont Report. April 18, 1979. Reading 3.2 Robert Bond, Christopher Fariss et al.A 61-million-person experiment in social influence and political mobilization. Nature 2012. PA1 due R2 due R3 assigned PA2 assigned
6 04/04 IRB (Cheryl Danton)      
7 04/09 Discussion 1/3 optimization vs generalization, training and test data, models, learning Reading 4.1: Nithya Sambasivan et al. Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. CHI 2021. 4.2 Wendy Parker Model Evaluation: An Adequacy-for-Purpose View. 2022 (read the introduction and (optionally) the rest) R3 due R4 assigned
8 04/11 Machine Learning in the Wild training data, feature engineering, information leakage, concept drift, algorithmic decision making   PA2 due
9 04/16 Fairness and Interpretability in Machine Learning fairness definitions Reading 5.1 Deirdre K. Mulligan, Joshua A. Kroll, Nitin Kohli, Richmond Y. Wong This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology CSCW 2019 Reading 5.2 Julia Angwin, Jeff Larson, Surya Mattu, Lauren Kirchner. Machine Bias. ProPublica, May 23, 2016 R4 due R5 assigned PA3 assigned
10 04/18 Visualization and Communication packaging data products, reproducibility, repeatability, visualization, communication    
11 04/23 Discussion 2/3     R5 due
12 04/25 Introduction to Privacy 1/2 privacy definitions, law, technology   PA3 due
13 04/30 Introduction to Privacy 2/2 data anonymization and deanonymization, k-anonimity, attacks, indigenous data sovereignty Reading 6: Shoshana Zuboff. Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology 2015. R6 assigned
14 05/02 Statistical Data Privacy differential privacy, sensitivity   PA4 assigned
15 05/07 Data Flows, Lifecyles, Data Markets provenance, right to be forgotten, data portability, data brokers, data ownership, value of data, data unions, cooperatives, strikes Reading 7 . Edith Ramirez, Julie Brill, Maureen K. Ohlhausen, Joshua D. Wright, Terrell McSweeny Data Brokers: A call for transparency and accountability. Federal Trade Commission, May, 2014 (Read Executive Summary and then Section 4 “Types of Products”) R6 due R7 assigned
16 05/09 Discussion 3/3      
17 05/14 Summary of the quarter via a case study of LLMs Statistics and the Census Bureau     R7 due PA4 due
18 05/16 AMA      
  05/17 No class     P due