HCDS (Fall 2017)/Schedule

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Last updated on 08/08/2018 by Jtmorgan

Week 1: September 28

Day 1 plan

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.

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Week 2: October 5

Day 2 plan

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

• 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
• Zetter, Kim. Arvind Narayanan Isn’t Anonymous, and Neither Are You. WIRED, 2012.

Homework assigned

• Reading reflection
• A1: privacy and anonymity

Resources

• 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.
• Gray, Mary. When Science, Customer Service, and Human Subjects Research Collide. Now What? Culture Digitally, 2014.
• Markham, Annette and Buchanan, Elizabeth. Ethical Decision-Making and Internet Researchers. Association for Internet Research, 2012.
• Tene, Omer and Polonetsky, Jules. Privacy in the Age of Big Data. Stanford Law Review, 2012.
• 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.
• Fiesler, Casey. Law & Ethics of Scraping: What HiQ v LinkedIn Could Mean for Researchers Violating TOS. Medium, 2017.
• EU General Data Protection Regulation.
• Green, Matthew. What is Differential Privacy? A Few Thoughts on Cryptographic Engineering, 2016.
• Hsu, Danny. Techniques to Anonymize Human Data. Data Sift, 2015.

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Week 3: October 12

Day 3 plan

Data provenance, preparation, and reproducibility

data curation, preservation, documentation, and archiving; best practices for open scientific research

Assignments due

• Week 2 reading reflection
• A1: Privacy and anonymity

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

Homework assigned

• Reading reflection
• A2: Data curation

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.
• Chapman et al. Cross Industry Standard Process for Data Mining. IBM, 2000.

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Week 4: October 19

Day 4 plan

Study design

understanding your data; framing research questions; planning your study

Assignments due

• Reading reflection
• A2: 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

Homework assigned

• Reading reflection
• A3: Measuring bias in data

Resources

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Week 5: October 26

Day 5 plan

Machine learning

ethical AI, algorithmic transparency, societal implications of machine learning

Assignments due

• Reading reflection
• A3: Measuring bias in data

Agenda

• Social implications of machine learning
• Consequences of algorithmic bias
• Sources of algorithmic bias
• Addressing algorithmic bias
• Auditing algorithms

Readings assigned

Homework assigned

• Reading reflection
• A4: Auditing algorithms

Resources

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Week 6: November 2

Day 6 plan

Mixed-methods research

Big data vs thick data; qualitative research in data science

Assignments due

• Reading reflection
• A4: Auditing algorithms

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
• A5: Final project plan

Resources

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Week 7: November 9

Day 7 plan

Human computation

ethics of crowdwork, crowdsourcing methodologies for analysis, design, and evaluation

Assignments due

• Reading reflection
• A5: 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
• A6: Self-ethnography

Resources

• go here

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Week 8: November 16

Day 8 plan

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

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Week 9: November 23

Day 9 plan

Human-centered data science in the wild

community data science; data science for social good

Assignments due

• Reading reflection
• A6: Crowdwork self-ethnography

Agenda

• NO CLASS - work on your own

Readings assigned

Homework assigned

• Reading reflection

Resources

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Week 10: November 30

Day 10 plan

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
• A7: Final presentation

Resources

• one

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Week 11: December 7

Day 11 plan

Future of human centered data science

case studies from research, industry, and policy; final presentations

Assignments due

• Reading reflection
• Final presentation

Agenda

• future directions of of human centered data science
• final presentations

Readings assigned

• none!

Homework assigned

• none!

Resources

• one

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Week 12: Finals Week

• NO CLASS
• FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 10
• LATE PROJECT SUBMISSIONS NOT ACCEPTED.