Human Centered Data Science (Fall 2018)/Schedule

This page is a work in progress.

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

• 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.

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.

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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
• 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.

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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: 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

• Chapter 2 "Assessing Reproducibility" and Chapter 3 "The Basic Reproducible Workflow Template" from The Practice of Reproducible Research University of California Press, 2018.
• 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.

See the datasets page for examples of well-documented and not-so-well documented open datasets.

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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

• 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 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

• Brian N Larson. 2017. Gender as a Variable in Natural-Language Processing: Ethical Considerations. EthNLP, 3: 30–40. Retrieved from http://www.ethicsinnlp.org/workshop/pdf/EthNLP04.pdf
• 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
• 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 (in Canvas)

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Week 5: October 25

Day 5 plan

Introduction to mixed-methods research

Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing

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

• Donovan, J., Caplan, R., Matthews, J., & Hanson, L. (2018). Algorithmic accountability: A primer. Data & Society, 501(c).

Homework assigned

• Reading reflection
• A3: Crowdwork ethnography

Resources

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

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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

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

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.

Homework assigned

• Reading reflection

Resources

• 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
• 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

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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

• Guest lecture: Rochelle LaPlante

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.

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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

• 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

• 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

• 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.

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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

• 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

• 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.

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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

• 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
• 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.
• 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.
• 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)

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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.