Human Centered Data Science (Fall 2018)/Schedule

This page is a work in progress.

Week 1: September 27

Day 1 plan

Introduction to Human Centered Data Science

What is data science? What is human centered? What is human centered data science?

Assignments due

• fill out the pre-course survey
• 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. (no reading reflection required)

Agenda

• Syllabus review
• Pre-course survey results
• What do we mean by data science?
• What do we mean by human centered?
• How does human centered design relate to data science?
• Looking ahead: Week 2 assignments and topics

Readings assigned

• 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

• Aragon, C. et al. (2016). Developing a Research Agenda for Human-Centered Data Science. Human Centered Data Science workshop, CSCW 2016.
• Kling, Rob and Star, Susan Leigh. Human Centered Systems in the Perspective of Organizational and Social Informatics. 1997.
• Ideo.org The Field Guide to Human-Centered Design. 2015.

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Week 2: October 4

Day 2 plan

Ethical considerations

privacy, informed consent and user treatment

Assignments due

• Week 1 reading reflection

Agenda

• Intro to assignment 1: Data Curation
• A brief history of research ethics
• Guest lecture: Javier Salido and Mark van Hollebeke, "A Practitioners View of Privacy & Data Protection"
• Guest lecture: Javier Salido, "Differential Privacy"
• Contextual Integrity in data science
• Week 2 reading reflection

Readings assigned

• Read: boyd, danah and Crawford, Kate, Six Provocations for Big Data (September 21, 2011). A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, September 2011. Available at SSRN: https://ssrn.com/abstract=1926431 or http://dx.doi.org/10.2139/ssrn.1926431

Homework assigned

• Reading reflection

Resources

• 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.
• Markham, Annette and Buchanan, Elizabeth. Ethical Decision-Making and Internet Researchers. Association for Internet Research, 2012.
• Hill, Kashmir. Facebook Manipulated 689,003 Users' Emotions For Science. Forbes, 2014.
• Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. 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.
• Hsu, Danny. Techniques to Anonymize Human Data. Data Sift, 2015.
• Metcalf, Jacob. Twelve principles of data ethics. Ethical Resolve, 2016.

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Week 3: October 11

Day 3 plan

Reproducibility and Accountability

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

Assignments due

• Week 2 reading reflection

Agenda

• Six Provocations for Big Data: Review & Reflections
• A primer on copyright, licensing, and hosting for code and data
• Introduction to replicability, reproducibility, and open research
• Reproducibility case study: fivethirtyeight.com
• Group activity: assessing reproducibility in data journalism
• Overview of Assignment 1: Data curation

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 18

Day 4 plan

Interrogating datasets

understanding your data; framing research questions; planning your study

Assignments due

• Reading reflection
• A1: Data curation

Agenda

• Final project: Goal, timeline, and deliverables.
• Overview of assignment 2: Bias in data
• Reading reflections review
• Sources of bias in datasets
• Introduction to assignment 2: Bias in data
• Sources of bias in data collection and processing
• In-class exercise: assessing bias in training data

Readings assigned

• Read: Duarte, N., Llanso, E., & Loup, A. (2018). Mixed Messages? The Limits of Automated Social Media Content Analysis. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81, 106. PDF: http://proceedings.mlr.press/v81/duarte18a.html
• Read: Bender, E. M., & Friedman, B. (2018). Data Statements for NLP: Toward Mitigating System Bias and Enabling Better Science. T0 appear in Transactions of the ACL. PDF: https://openreview.net/forum?id=By4oPeX9f

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 25

Day 5 plan

Interrogating algorithms

algorithmic transparency and accountability

Assignments due

• Reading reflection

Agenda

• Assignment 1 review & reflection
• Week 4 reading reflection discussion
• Survey of qualitative research methods
• Mixed-methods case study #1: The Wikipedia Gender Gap: causes & consequences
• In-class activity: Automated Gender Recognition scenarios
• Introduction to ethnography
• Ethnographic research case study: Structured data on Wikimedia Commons
• Introduction to crowdwork
• Overview of Assignment 3: Crowdwork ethnography

Readings assigned

• Read: 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.
• Read: Diakopoulos, N. (2014). Algorithmic accountability reporting: On the investigation of black boxes. Tow Center for Digital Journalism, 1–33. https://doi.org/10.1002/ejoc.201200111

Homework assigned

• Reading reflection

Resources

• 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.
• TO ADD: Propublica report

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Week 6: November 1

Day 6 plan

Day 6 slides

Introduction to mixed-methods research

Big data vs thick data; qualitative research in data science

Assignments due

• Reading reflection
• A2: Bias in data

Agenda

• Reading reflections
• Ethical implications of crowdwork
• Algorithmic transparency, interpretability, and accountability
• Auditing algorithms
• In-class activity: auditing the Perspective API

Readings assigned

• R. Stuart Geiger and Aaron Halfaker. 2017. Operationalizing conflict and cooperation between automated software agents in Wikipedia: A replication and expansion of Even Good Bots Fight. Proceedings of the ACM on Human-Computer Interaction (Nov 2017 issue, CSCW 2018 Online First) 1, 2, Article 49. DOI: https://doi.org/10.1145/3134684

Homework assigned

• Reading reflection
• A3: Crowdwork ethnography

Resources

• WeArDynamo contributors. How to be a good requester and Guidelines for Academic Requesters. Wearedynamo.org

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Week 7: November 8

Day 7 plan

Critical approaches to data science

power, data & society, ethics of crowdwork

Assignments due

• Reading reflection

Agenda

• Guest lecture: Rochelle LaPlante

Readings assigned (read both, reflect on one)

Homework assigned

• Reading reflection
• A4: Final project plan

Resources

• Neff, G., Tanweer, A., Fiore-Gartland, B., & Osburn, L. (2017). Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science. Big Data, 5(2), 85–97. https://doi.org/10.1089/big.2016.0050
• Wang, Tricia. Why Big Data Needs Thick Data. Ethnography Matters, 2016.
• Lilly C. Irani and M. Six Silberman. 2013. Turkopticon: interrupting worker invisibility in amazon mechanical turk. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). DOI: https://doi.org/10.1145/2470654.2470742
• Shilad Sen, Margaret E. Giesel, Rebecca Gold, Benjamin Hillmann, Matt Lesicko, Samuel Naden, Jesse Russell, Zixiao (Ken) Wang, and Brent Hecht. 2015. Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW '15). DOI: http://dx.doi.org/10.1145/2675133.2675285

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Week 8: November 15

Day 8 plan

Human-centered algorithm design

human-centered methods for designing and evaluating algorithmic systems

Assignments due

• Reading reflection
• A3: Crowdwork ethnography

Agenda

• Final project overview & examples
• Guest Lecture: Kelly Franznick, Blink UX
• Reading reflections
• Human-centered algorithm design

• design process
• user-driven evaluation
• design patterns & anti-patterns

Readings assigned

• Read: Baumer, E. P. S. (2017). Toward human-centered algorithm design. Big Data & Society, 4(2), 2053951717718854. https://doi.org/10.1177/2053951717718854
• Read: Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine, 35(4), 105. https://doi.org/10.1609/aimag.v35i4.2513

Homework assigned

• Reading reflection

Resources

• Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User perception of differences in recommender algorithms. In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14). ACM, New York, NY, USA, 161-168. DOI: https://doi.org/10.1145/2645710.2645737
• Chen, N., Brooks, M., Kocielnik, R., Hong, R., Smith, J., Lin, S., Qu, Z., Aragon, C. Lariat: A visual analytics tool for social media researchers to explore Twitter datasets. Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS), Data Analytics and Data Mining for Social Media Minitrack (2017)
• Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Making recommendations better: an analytic model for human-recommender interaction. In CHI '06 Extended Abstracts on Human Factors in Computing Systems (CHI EA '06). ACM, New York, NY, USA, 1103-1108. DOI=http://dx.doi.org/10.1145/1125451.1125660
• Kevin Crowston and the Gravity Spy Team. 2017. Gravity Spy: Humans, Machines and The Future of Citizen Science. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17 Companion). ACM, New York, NY, USA, 163-166. DOI: https://doi.org/10.1145/3022198.3026329
• Michael D. Ekstrand and Martijn C. Willemsen. 2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 221-224. DOI: https://doi.org/10.1145/2959100.2959179
• Jess Holbrook. Human Centered Machine Learning. Google Design Blog. 2017.
• Xavier Amatriain and Justin Basilico. Netflix Recommendations: Beyond the 5 stars. Netflix Tech Blog, 2012.
• Fabien Girardin. Experience design in the machine learning era. Medium, 2016.
• Brian Whitman. How music recommendation works - and doesn't work. Variogram, 2012.
• Paul Lamere. How good is Google's Instant Mix?. Music Machinery, 2011.
• Snyder, Jaime. Values in the Design of Visualizations. 2016 CSCW workshop on Human-Centered Data Science.

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Week 9: November 22 (No Class Session)

Day 9 plan

Data science for social good

TBD

Assignments due

• Reading reflection
• A4: Crowdwork ethnography

Agenda

• Reading reflections discussion
• Feedback on Final Project Plans
• Guest lecture: Steven Drucker (Microsoft Research)
• UI patterns & UX considerations for ML/data-driven applications
• Final project presentation: what to expect
• In-class activity: final project peer review

Readings assigned

• Hill, B. M., Dailey, D., Guy, R. T., Lewis, B., Matsuzaki, M., & Morgan, J. T. (2017). Democratizing Data Science: The Community Data Science Workshops and Classes. In N. Jullien, S. A. Matei, & S. P. Goggins (Eds.), Big Data Factories: Scientific Collaborative approaches for virtual community data collection, repurposing, recombining, and dissemination. New York, New York: Springer Nature. [Preprint/Draft PDF]
• Bivens, R. and Haimson, O.L. 2016. Baking Gender Into Social Media Design: How Platforms Shape Categories for Users and Advertisers. Social Media + Society. 2, 4 (2016), 205630511667248. DOI:https://doi.org/10.1177/2056305116672486.
• Schlesinger, A. et al. 2017. Intersectional HCI: Engaging Identity through Gender, Race, and Class. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17. (2017), 5412–5427. DOI:https://doi.org/10.1145/3025453.3025766.

Homework assigned

• Reading reflection

Resources

• Berney, Rachel, Bernease Herman, Gundula Proksch, Hillary Dawkins, Jacob Kovacs, Yahui Ma, Jacob Rich, and Amanda Tan. Visualizing Equity: A Data Science for Social Good Tool and Model for Seattle. Data Science for Social Good Conference, September 2017, Chicago, Illinois USA (2017).
• Sayamindu Dasgupta and Benjamin Mako Hill. Learning With Data: Designing for Community Introspection and Exploration. Position paper for Developing a Research Agenda for Human-Centered Data Science (a CSCW 2016 workshop).

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Week 10: November 29

Day 10 plan

Day 10 slides

User experience and big data

Assignments due

• Reading reflection

Agenda

• Reading reflections discussion
• Feedback on Final Project Plans
• Guest lecture: Steven Drucker (Microsoft Research)
• UI patterns & UX considerations for ML/data-driven applications
• Final project presentation: what to expect
• In-class activity: final project peer review

Readings assigned

• Megan Risdal, Communicating data science: a guide to presenting your work. Kaggle blog, 2016.
• Marilynn Larkin, How to give a dynamic scientific presentation. Elsevier Connect, 2015.

Homework assigned

• Reading reflection
• A5: Final presentation

Resources

• Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22, 4-5 (October 2012), 441-504. DOI=http://dx.doi.org/10.1007/s11257-011-9118-4
• Sean M. McNee, Nishikant Kapoor, and Joseph A. Konstan. 2006. Don't look stupid: avoiding pitfalls when recommending research papers. In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work (CSCW '06). ACM, New York, NY, USA, 171-180. DOI=http://dx.doi.org/10.1145/1180875.1180903
• Megan Risdal, Communicating data science: Why and how to visualize information. Kaggle blog, 2016.
• Megan Risdal, Communicating data science: an interview with a storytelling expert. Kaggle blog, 2016.
• Richard Garber, Power of brief speeches: World War I and the Four Minute Men. Joyful Public Speaking, 2010.
• Brent Dykes, Data Storytelling: The Essential Data Science Skill Everyone Needs. Forbes, 2016.

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Week 11: December 6

Day 11 plan

Final presentations

course wrap up, presentation of student projects

Assignments due

• Reading reflection
• A5: Final presentation

Agenda

• Student final presentations
• Course wrap-up

Readings assigned

• none!

Homework assigned

• none!

Resources

• one

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Week 12: Finals Week (No Class Session)

• NO CLASS
• A6: FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 9
• LATE PROJECT SUBMISSIONS NOT ACCEPTED.