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.

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.

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

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

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

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

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

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 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

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

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

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.

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

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

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.

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

NONE

Homework assigned

A5: Final presentation

Resources

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.

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)

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

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.