Human Centered Data Science (Fall 2018)/Schedule



Week 1: September 27
Day 1 plan

Day 1 slides


 * 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.


 * 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.
 * Harford, T. (2014). Big data: A big mistake? Significance, 11(5), 14–19.

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
 * A1: Data curation


 * Resources
 * Nissenbaum, Helen, Privacy as Contextual Integrity
 * 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.
 * Bethan Cantrell, Javier Salido, and Mark Van Hollebeke (2016). Industry needs to embrace data ethics: Here's how it could be done. Workshop on Data and Algorithmic Transparency (DAT'16). http://datworkshop.org/
 * Javier Salido (2012). Differential Privacy for Everyone. Microsoft Corporation Whitepaper.
 * 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. 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.
 * 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

Day 3 slides


 * 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: 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.


 * Homework assigned
 * Reading reflection


 * Resources
 * Hickey, Walt. The Dollars and Cents Case Against Hollywood's Exclusion of Women. FiveThirtyEight, 2014.
 * Keegan, Brian. The Need for Openness in Data Journalism. 2014.
 * Hickey, Walt. The Bechdel Test: Checking Our Work. FiveThirtyEight, 2014.
 * J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), Altmetrics: A manifesto, 26 October 2010.


 * Assignment 1 Data curation resources:


 * Chapter 2 "Assessing Reproducibility" and Chapter 3 "The Basic Reproducible Workflow Template" from The Practice of Reproducible Research University of California Press, 2018.
 * sample code for API calls (view the notebook, download the notebook).
 * See the datasets page for examples of well-documented and not-so-well documented open datasets.

Week 4: October 18
Day 4 plan

Day 4 slides


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


 * Assignments due
 * Reading reflection
 * A1: Data curation


 * Agenda


 * Readings assigned (Read both, reflect on one)
 * Wang, Tricia. Why Big Data Needs Thick Data. Ethnography Matters, 2016.
 * 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)


 * Homework assigned
 * Reading reflection
 * A2: Bias in data


 * Resources
 * Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016). ''Social data: Biases, methodological pitfalls, and ethical boundaries.
 * Brian N Larson. 2017. ''Gender as a Variable in Natural-Language Processing: Ethical Considerations. EthNLP, 3: 30–40.
 * Bender, E. M., & Friedman, B. (2018). Data Statements for NLP: Toward Mitigating System Bias and Enabling Better Science. To appear in Transactions of the ACL.
 * Isaac L. Johnson, Yilun Lin, Toby Jia-Jun Li, Andrew Hall, Aaron Halfaker, Johannes Schöning, and Brent Hecht. 2016. Not at Home on the Range: Peer Production and the Urban/Rural Divide. CHI '16. DOI: https://doi.org/10.1145/2858036.2858123
 * Leo Graiden Stewart, Ahmer Arif, A. Conrad Nied, Emma S. Spiro, and Kate Starbird. 2017. Drawing the Lines of Contention: Networked Frame Contests Within #BlackLivesMatter Discourse. Proc. ACM Hum.-Comput. Interact. 1, CSCW, Article 96 (December 2017), 23 pages. DOI: https://doi.org/10.1145/3134920
 * Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. No country for old members: user lifecycle and linguistic change in online communities. In Proceedings of the 22nd international conference on World Wide Web (WWW '13). ACM, New York, NY, USA, 307-318. DOI: https://doi.org/10.1145/2488388.2488416

Week 5: October 25
Day 5 plan

Day 5 slides


 * Introduction to mixed-methods research: Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing


 * Assignments due
 * Reading reflection


 * Agenda


 * Readings assigned (Read both, reflect on one)
 * Donovan, J., Caplan, R., Matthews, J., & Hanson, L. (2018). Algorithmic accountability: A primer. Data & Society, 501(c).


 * Homework assigned
 * Reading reflection
 * A3: Crowdwork ethnography


 * Qualitative research methods resources
 * Ladner, S. (2016). Practical ethnography: A guide to doing ethnography in the private sector. Routledge.
 * Spradley, J. P. (2016). The ethnographic interview. Waveland Press.
 * Eriksson, P., & Kovalainen, A. (2015). Ch 12: Ethnographic Research. In Qualitative methods in business research: A practical guide to social research. Sage.
 * Usability.gov, System usability scale.
 * Nielsen, Jakob (2000). Why you only need to test with five users. nngroup.com.


 * Wikipedia gender gap research resources
 * Hill, B. M., & Shaw, A. (2013). [journals.plos.org/plosone/article?id=10.1371/journal.pone.0065782 The Wikipedia gender gap revisited: Characterizing survey response bias with propensity score estimation]. PloS one, 8(6), e65782
 * 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
 * Maximillian Klein. Gender by Wikipedia Language. Wikidata Human Gender Indicators (WHGI), 2017.
 * Source: Wagner, C., Garcia, D., Jadidi, M., & Strohmaier, M. (2015, April). It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia. In ICWSM (pp. 454-463).


 * 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


 * Crowdwork research resources
 * WeArDynamo contributors. How to be a good requester and Guidelines for Academic Requesters. Wearedynamo.org

Week 6: November 1
Day 6 plan


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


 * Assignments due
 * Reading reflection
 * A2: Bias in data


 * Agenda


 * Readings assigned
 * Astrid Mager. 2012. Algorithmic ideology: How capitalist society shapes search engines. Information, Communication & Society 15, 5: 769–787. http://doi.org/10.1080/1369118X.2012.676056


 * Homework assigned
 * Reading reflection


 * Resources
 * 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.
 * Shahriari, K., & Shahriari, M. (2017). IEEE standard review - Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems. Institute of Electrical and Electronics Engineers
 * ACM US Policy Council Statement on Algorithmic Transparency and Accountability. January 2017.
 * Asilomar AI Principles. Future of Life Institute, 2017.
 * Diakopoulos, N., Friedler, S., Arenas, M., Barocas, S., Hay, M., Howe, B., … Zevenbergen, B. (2018). Principles for Accountable Algorithms and a Social Impact Statement for Algorithms. Fatml.Org 2018.
 * Friedman, B., & Nissenbaum, H. (1996). Bias in Computer Systems. ACM Trans. Inf. Syst., 14(3), 330–347.
 * 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
 * Nate Matias, 2017. How Anyone Can Audit Facebook's Newsfeed. Medium.com
 * 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.
 * Paul Lamere. How good is Google's Instant Mix?. Music Machinery, 2011.
 * Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. ''Machine Bias: Risk Assessment in Criminal Sentencing. Propublica, May 2018.
 * Google's Perspective API

Week 7: November 8
Day 7 plan


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


 * Assignments due
 * Reading reflection
 * A3: Crowdwork ethnography


 * Agenda


 * Readings assigned (read both, reflect on one)
 * Read: Baumer, E. P. S. (2017). Toward human-centered algorithm design. Big Data & Society.
 * 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.


 * 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
 * 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
 * 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.

Week 8: November 15
Day 8 plan


 * Human-centered algorithm design: algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems


 * Assignments due
 * Reading reflection


 * 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.


 * Homework assigned
 * Reading reflection


 * Resources
 * Ethical OS Toolkit and Risk Mitigation Checklist. EthicalOS.org.
 * Morgan, J. 2016. Evaluating Related Articles recommendations. Wikimedia Research.
 * Morgan, J. 2017. Comparing most read and trending edits for the top articles feature. Wikimedia Research.
 * 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).
 * 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).
 * 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).
 * 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).
 * Jess Holbrook. Human Centered Machine Learning. Google Design Blog. 2017.
 * Anderson, Carl. The role of model interpretability in data science. Medium, 2016.

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


 * Data science for social good: Community-based and participatory approaches to data science; Using data science for society's benefit


 * Assignments due
 * Reading reflection
 * A4: Final project plan


 * Agenda


 * Readings assigned
 * 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).


 * Homework assigned
 * Reading reflection


 * Resources
 * Daniela Aiello, Lisa Bates, et al. Eviction Lab Misses the Mark, ShelterForce, August 2018.

Week 10: November 29
Day 10 plan


 * User experience and big data: Design considerations for machine learning applications; human centered data visualization; data storytelling


 * Assignments due
 * Reading reflection


 * Agenda


 * Readings assigned
 * NONE


 * Homework assigned
 * A5: Final presentation


 * Resources
 * Fabien Girardin. Experience design in the machine learning era. Medium, 2016.
 * Xavier Amatriain and Justin Basilico. Netflix Recommendations: Beyond the 5 stars. Netflix Tech Blog, 2012.
 * Jess Holbrook. Human Centered Machine Learning. Google Design Blog. 2017.
 * 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
 * Patrick Austin, Facebook, Google, and Microsoft Use Design to Trick You Into Handing Over Your Data, New Report Warns. Gizmodo, 6/18/2018
 * Brown, A., Tuor, A., Hutchinson, B., & Nichols, N. (2018). [Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection. arXiv preprint arXiv:1803.04967.
 * Cremonesi, P., Elahi, M., & Garzotto, F. (2017). User interface patterns in recommendation-empowered content intensive multimedia applications. Multimedia Tools and Applications, 76(4), 5275-5309.
 * Marilynn Larkin, How to give a dynamic scientific presentation. Elsevier Connect, 2015.
 * Megan Risdal, Communicating data science: a guide to presenting your work. Kaggle blog, 2016.
 * 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.
 * 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
 * A5: Final presentation


 * Agenda


 * Readings assigned
 * none!


 * Homework assigned
 * A6: Final project report (due 12/9 by 11:59pm)


 * 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.