Human Centered Data Science (Fall 2018)/Schedule



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


 * 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.

Week 2: October 4
Day 2 plan


 * Ethical considerations: privacy, informed consent and user treatment


 * Assignments due
 * Week 1 reading reflection


 * Agenda


 * 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.

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


 * 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.
 * Narayan, Sneha et al. Replication Data for: The Wikipedia Adventure: Field Evaluation of an Interactive Tutorial for New Users. Harvard Dataverse, 2017.
 * * Warnke-Wang, Morten. Autoconfirmed article creation trial. Wikimedia, 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 18
Day 4 plan


 * Interrogating datasets: bias in data; best practices for selecting, describing, and implementing training data


 * Assignments due
 * Reading reflection
 * A1: Data curation


 * Agenda


 * 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.
 * 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
 * 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 25
Day 5 plan


 * Interrogating algorithms: algorithmic transparency and accountability; methods and contexts for algorithmic audits


 * Assignments due
 * Reading reflection


 * Agenda


 * 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

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


 * 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

Week 7: November 8
Day 7 plan


 * Critical approaches to data science: power, data & society, ethics of crowdwork


 * Assignments due
 * Reading reflection


 * Agenda


 * 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

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


 * 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.

Week 9: November 22 (No Class Session)
Day 9 plan


 * Data science for social good: TBD


 * Assignments due
 * Reading reflection
 * A4: Crowdwork ethnography


 * Agenda


 * 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).

Week 10: November 29
Day 10 plan

Day 10 slides


 * User experience and big data''


 * Assignments due
 * Reading reflection


 * Agenda


 * 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.

Week 11: December 6
Day 11 plan


 * Final presentations: course wrap up, presentation of student projects''


 * Assignments due
 * Reading reflection
 * A5: Final presentation


 * Agenda


 * Readings assigned
 * none!


 * Homework assigned
 * none!


 * Resources
 * one

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.