This page is a work in progress.
Week 1: September 28
- Assignments due
- fill out the pre-course survey
- Agenda
- Course overview & orientation
- What do we mean by "data science?"
- What do we mean by "human centered?"
- How does human centered design relate to data science?
- Readings assigned
- Watch: Why Humans Should Care About Data Science (Cecilia Aragon, 2016 HCDE Seminar Series)
- Read: Aragon, C. et al. (2016). Developing a Research Agenda for Human-Centered Data Science. Human Centered Data Science workshop, CSCW 2016.
- Read: Provost, Foster, and Tom Fawcett. Data science and its relationship to big data and data-driven decision making. Big Data 1.1 (2013): 51-59.
- Read: Kling, Rob and Star, Susan Leigh. Human Centered Systems in the Perspective of Organizational and Social Informatics. 1997.
- Homework assigned
- Reading reflection
- Resources
- Ideo.org The Field Guide to Human-Centered Design. 2015.
- Faraway, Julian. The Decline and Fall of Statistics. Faraway Statistics, 2015.
- Press, Gil. Data Science: What's The Half-Life Of A Buzzword? Forbes, 2013.
- Bloor, Robin. A Data Science Rant. Inside Analysis, 2013.
- Various authors. Position papers from 2016 CSCW Human Centered Data Science Workshop. 2016.
Week 2: October 5
Ethical considerations in Data Science: privacy, informed consent and user treatment
- Assignments due
- Week 1 reading reflection
- Agenda
- Informed consent in the age of Data Science
- Privacy
- User expectations
- Inferred information
- Correlation
- Anonymisation strategies
- Readings assigned
- Read: Markham, Annette and Buchanan, Elizabeth. Ethical Decision-Making and Internet Researchers. Association for Internet Research, 2012.
- Read: Barocas, Solan and Nissenbaum, Helen. Big Data's End Run around Anonymity and Consent. In Privacy, Big Data, and the Public Good. 2014. (PDF on Canvas)
- Homework assigned
- Reading reflection
- Resources
- Wittkower, D.E. Lurkers, creepers, and virtuous interactivity: From property rights to consent and care as a conceptual basis for privacy concerns and information ethics
- National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report. U.S. Department of Health and Human Services, 1979.
- Hill, Kashmir. Facebook Manipulated 689,003 Users' Emotions For Science. Forbes, 2014.
- Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock Experimental evidence of massive-scale emotional contagion through social networks. PNAS 2014 111 (24) 8788-8790; published ahead of print June 2, 2014.
- Barbaro, Michael and Zeller, Tom. A Face Is Exposed for AOL Searcher No. 4417749. New York Times, 2008.
- Zetter, Kim. Arvind Narayanan Isn’t Anonymous, and Neither Are You. WIRED, 2012.
- Gray, Mary. When Science, Customer Service, and Human Subjects Research Collide. Now What? Culture Digitally, 2014.
- Tene, Omer and Polonetsky, Jules. Privacy in the Age of Big Data. Stanford Law Review, 2012.
- Dwork, Cynthia. Differential Privacy: A survey of results. Theory and Applications of Models of Computation , 2008.
- Green, Matthew. What is Differential Privacy? A Few Thoughts on Cryptographic Engineering, 2016.
- Hsu, Danny. Techniques to Anonymize Human Data. Data Sift, 2015.
- Metcalf, Jacob. Twelve principles of data ethics. Ethical Resolve, 2016.
- Poor, Nathaniel and Davidson, Roei. When The Data You Want Comes From Hackers, Or, Looking A Gift Horse In The Mouth. CSCW Human Centered Data Science Workshop, 2016.
Week 3: October 12
- Data provenance, preparation, and reproducibility
- data curation, preservation, documentation, and archiving; best practices for open scientific research
- Assignments due
- Week 2 reading reflection
- Agenda
- Final project overview
- Introduction to open research
- Understanding data licensing and attribution
- Supporting replicability and reproducibility
- Making your research and data accessible
- Working with Wikipedia datasets
- Assignment 1 description
- Readings assigned
- Read: Chapter 2 "Assessing Reproducibility" and Chapter 3 "The Basic Reproducible Workflow Template" from The Practice of Reproducible Research University of California Press, 2018.
- Read: Hickey, Walt. The Dollars and Cents Case Against Hollywood's Exclusion of Women. FiveThirtyEight, 2014. AND Keegan, Brian. The Need for Openness in Data Journalism. 2014.
- Homework assigned
- Reading reflection
- A1: Data curation
- Examples of well-documented open research projects
- Keegan, Brian. WeatherCrime. GitHub, 2014.
- Geiger, Stuart R. and Halfaker, Aaron. Operationalizing conflict and cooperation between automated software agents in Wikipedia: A replication and expansion of "Even Good Bots Fight". GitHub, 2017.
- Thain, Nithum; Dixon, Lucas; and Wulczyn, Ellery. Wikipedia Talk Labels: Toxicity. Figshare, 2017.
- Narayan, Sneha et al. Replication Data for: The Wikipedia Adventure: Field Evaluation of an Interactive Tutorial for New Users. Harvard Dataverse, 2017.
- Examples of not-so-well documented open research projects
- Eclarke. SWGA paper. GitHub, 2016.
- David Lefevre. Lefevre and Cox: Delayed instructional feedback may be more effective, but is this contrary to learners’ preferences? Figshare, 2016.
- Alneberg. CONCOCT Paper Data. GitHub, 2014.
- Other resources
- Press, Gil. Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says. Forbes, 2016.
- Christensen, Garret. Manual of Best Practices in Transparent Social Science Research. 2016.
- Hickey, Walt. The Bechdel Test: Checking Our Work. FiveThirtyEight, 2014.
- Chapman et al. Cross Industry Standard Process for Data Mining. IBM, 2000.
Week 4: October 19
- Study design
- understanding your data; framing research questions; planning your study
- Assignments due
- Reading reflection
- A1: Data curation
- Agenda
- How Wikipedia works (and how it doesn't)
- guest speaker: Morten Warnke-Wang, Wikimedia Foundation
- Sources of bias in data science research
- Sources of bias in Wikipedia data
- Readings assigned
- Shyong (Tony) K. Lam, Anuradha Uduwage, Zhenhua Dong, Shilad Sen, David R. Musicant, Loren Terveen, and John Riedl. 2011. WP:clubhouse?: an exploration of Wikipedia's gender imbalance. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym '11). ACM, New York, NY, USA, 1-10. DOI=http://dx.doi.org/10.1145/2038558.2038560
- Homework assigned
- Reading reflection
- A2: Bias in data
- Resources
- Aschwanden, Christie. Science Isn't Broken FiveThirtyEight, 2015.
- Halfaker, Aaron et al. The Rise and Decline of an Open Collaboration Community: How Wikipedia's reaction to sudden popularity is causing its decline. American Behavioral Scientist, 2012.
- Warnke-Wang, Morten. Autoconfirmed article creation trial. Wikimedia, 2017.
- Wikipedia Or Encyclopædia Britannica: Which Has More Bias?. Forbes, 2015. Based on Greenstein, Shane, and Feng Zhu.Do Experts or Collective Intelligence Write with More Bias? Evidence from Encyclopædia Britannica and Wikipedia. Harvard Business School working paper.
Week 5: October 26
- Machine learning
- ethical AI, algorithmic transparency, societal implications of machine learning
- Assignments due
- Reading reflection
- A2: Bias in data
- Agenda
- Social implications of machine learning
- Consequences of algorithmic bias
- Sources of algorithmic bias
- Addressing algorithmic bias
- Auditing algorithms
- Readings assigned
- Christian Sandvig, Kevin Hamilton, Karrie Karahalios, Cedric Langbort (2014/05/22) Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms. Paper presented to "Data and Discrimination: Converting Critical Concerns into Productive Inquiry," a preconference at the 64th Annual Meeting of the International Communication Association. May 22, 2014; Seattle, WA, USA.
- Homework assigned
- Reading reflection
- A3: Final project plan
- Resources
- Bamman, David Interpretability in Human-Centered Data Science. 2016 CSCW workshop on Human-Centered Data Science.
- Blue, Violet. Google’s comment-ranking system will be a hit with the alt-right. Engadget, 2017.
- Ingold, David and Soper, Spencer. Amazon Doesn’t Consider the Race of Its Customers. Should It?. Bloomberg, 2016.
- Mars, Roman. The Age of the Algorithm. 99% Invisible Podcast, 2017.
Week 6: November 2
- Mixed-methods research
- Big data vs thick data; qualitative research in data science
- Assignments due
- Reading reflection
- Agenda
- Guest speakers: Aaron Halfaker, Caroline Sinders (Wikimedia Foundation)
- Mixed methods research
- Ethnographic methods in data science
- Project plan brainstorm/Q&A session
- Readings assigned
- Homework assigned
- Reading reflection
- Resources
Week 7: November 9
- Human computation
- ethics of crowdwork, crowdsourcing methodologies for analysis, design, and evaluation
- Assignments due
- Reading reflection
- A3: Final project plan
- Agenda
- the role of qualitative research in human centered data science
- scaling qualitative research through crowdsourcing
- types of crowdwork
- ethical and practical considerations for crowdwork
- Introduction to assignment 4: Mechanical Turk ethnography
- Readings assigned
- Homework assigned
- Reading reflection
- A4: Crowdwork self-ethnography
- Resources
- go here
Week 8: November 16
- User experience and big data
- prototyping and user testing; benchmarking and iterative evaluation; UI design for data science
- Assignments due
- Reading reflection
- Agenda
- HCD process in the design of data-driven applications
- understanding user needs, user intent, and context of use in recommender system design
- trust, empowerment, and seamful design
- HCD in data analysis and visualization
- final project lightning feedback sessions
- Readings assigned
- Homework assigned
- Reading reflection
- Resources
Snyder, Jaime. Values in the Design of Visualizations. 2016 CSCW workshop on Human-Centered Data Science.
Week 9: November 23
- Human-centered data science in the wild
- community data science; data science for social good
- Assignments due
- Reading reflection
- A4: Crowdwork self-ethnography
- Agenda
- NO CLASS - work on your own
- Readings assigned
- Homework assigned
- Reading reflection
- Resources
Week 10: November 30
- Communicating methods, results, and implications
- translating for non-data scientists
- Assignments due
- Reading reflection
- Agenda
- communicating about your research effectively and honestly to different audiences
- publishing your research openly
- disseminating your research
- final project workshop
- Readings assigned
- Homework assigned
- Reading reflection
- A5: Final presentation
- Resources
- one
Week 11: December 7
- Future of human centered data science
- case studies from research, industry, and policy; final presentations
- Assignments due
- Reading reflection
- A5: Final presentation
- Agenda
- future directions of of human centered data science
- final presentations
- Readings assigned
- none!
- Homework assigned
- none!
- Resources
- one
Week 12: Finals Week
- NO CLASS
- A6: FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 10
- LATE PROJECT SUBMISSIONS NOT ACCEPTED.
