HCDS (Fall 2017)/Schedule

This page is a work in progress.

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

• 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.

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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

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.

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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
• 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.

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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

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.
• Anderson, Carl. The role of model interpretability in data science. Medium, 2016.
• Hill, Kashmir. Facebook figured out my family secrets, and it won't tell me how. Engadget, 2017.
• 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.
• Whitman, Brian. How music recommendation works - and doesn't work. Variogram, 2012.
• Lamere, Paul. How good is Google's Instant Mix?. Music Machinery, 2011.
• Mars, Roman. The Age of the Algorithm. 99% Invisible Podcast, 2017.
• Google's Perspective API

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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
• A2: Bias in data

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

• Maximillian Klein. Gender by Wikipedia Language. Wikidata Human Gender Indicators (WHGI), 2017.
• Benjamin Collier and Julia Bear. Conflict, criticism, or confidence: an empirical examination of the gender gap in wikipedia contributions. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW '12). DOI: https://doi.org/10.1145/2145204.2145265
• Christina Shane-Simpson, Kristen Gillespie-Lynch, Examining potential mechanisms underlying the Wikipedia gender gap through a collaborative editing task, In Computers in Human Behavior, Volume 66, 2017, https://doi.org/10.1016/j.chb.2016.09.043. (PDF on Canvas)
• Amanda Menking and Ingrid Erickson. 2015. The Heart Work of Wikipedia: Gendered, Emotional Labor in the World's Largest Online Encyclopedia. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). https://doi.org/10.1145/2702123.2702514
• Andrea Forte, Nazanin Andalibi, and Rachel Greenstadt. Privacy, Anonymity, and Perceived Risk in Open Collaboration: A Study of Tor Users and Wikipedians. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17). DOI: https://doi.org/10.1145/2998181.2998273
• Wang, Tricia. Why Big Data Needs Thick Data. Ethnography Matters, 2016.
• Operationalizing Conflict and Cooperation between Automated Software Agents in Wikipedia: A Replication and Expansion of “Even Good Bots Fight”

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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
• 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

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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

Snyder, Jaime. Values in the Design of Visualizations. 2016 CSCW workshop on Human-Centered Data Science.

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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
• A4: 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
• A5: 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
• A5: 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
• A6: FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 10
• LATE PROJECT SUBMISSIONS NOT ACCEPTED.