HCDS (Fall 2017)/Schedule: Difference between revisions

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=== Week 1: September 28 ===
=== Week 1: September 28 ===
[[HCDS_(Fall_2017)/Day_1_plan|Day 1 plan]]
[[HCDS_(Fall_2017)/Day_1_plan|Day 1 plan]]
[[:File:HCDS Week 1 slides.pdf|Day 1 slides]]
;Course overview: ''What is data science? What is human centered? What is human centered data science?''


;Assignments due
;Assignments due
Line 50: Line 56:
[[HCDS_(Fall_2017)/Day_2_plan|Day 2 plan]]
[[HCDS_(Fall_2017)/Day_2_plan|Day 2 plan]]


Ethical considerations in Data Science: privacy, informed consent and user treatment
[[:File:HCDS Week 2 slides.pdf|Day 2 slides]]
 
;Ethical considerations in Data Science: ''privacy, informed consent and user treatment''




Line 87: Line 95:
=== Week 3: October 12 ===
=== Week 3: October 12 ===
[[HCDS_(Fall_2017)/Day_3_plan|Day 3 plan]]
[[HCDS_(Fall_2017)/Day_3_plan|Day 3 plan]]
[[:File:HCDS Week 3 slides.pdf|Day 3 slides]]


;Data provenance, preparation, and reproducibility: ''data curation, preservation, documentation, and archiving; best practices for open scientific research''
;Data provenance, preparation, and reproducibility: ''data curation, preservation, documentation, and archiving; best practices for open scientific research''
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=== Week 4: October 19 ===
=== Week 4: October 19 ===
[[HCDS_(Fall_2017)/Day_4_plan|Day 4 plan]]
[[HCDS_(Fall_2017)/Day_4_plan|Day 4 plan]]
[[:File:HCDS Week 4 slides.pdf|Day 4 slides]]


;Study design: ''understanding your data; framing research questions; planning your study''
;Study design: ''understanding your data; framing research questions; planning your study''
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=== Week 5: October 26 ===
=== Week 5: October 26 ===
[[HCDS_(Fall_2017)/Day_5_plan|Day 5 plan]]
[[HCDS_(Fall_2017)/Day_5_plan|Day 5 plan]]
[[:File:HCDS Week 5 slides.pdf|Day 5 slides]]


;Machine learning: ''ethical AI, algorithmic transparency, societal implications of machine learning''
;Machine learning: ''ethical AI, algorithmic transparency, societal implications of machine learning''
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;Assignments due
;Assignments due
* Reading reflection
* Reading reflection
* A2: Bias in data


;Agenda
;Agenda
Line 178: Line 191:


;Resources
;Resources
Bamman, David ''[https://cscw2016hcds.files.wordpress.com/2015/10/bamman_hcds.pdf Interpretability in Human-Centered Data Science].'' 2016 CSCW workshop on Human-Centered Data Science.
* Bamman, David ''[https://cscw2016hcds.files.wordpress.com/2015/10/bamman_hcds.pdf Interpretability in Human-Centered Data Science].'' 2016 CSCW workshop on Human-Centered Data Science.
* Anderson, Carl. ''[https://medium.com/@leapingllamas/the-role-of-model-interpretability-in-data-science-703918f64330 The role of model interpretability in data science].'' Medium, 2016.
* Hill, Kashmir. ''[https://gizmodo.com/facebook-figured-out-my-family-secrets-and-it-wont-tel-1797696163 Facebook figured out my family secrets, and it won't tell me how].'' Engadget, 2017.
* Blue, Violet. ''[https://www.engadget.com/2017/09/01/google-perspective-comment-ranking-system/ Google’s comment-ranking system will be a hit with the alt-right].'' Engadget, 2017.
* Ingold, David and Soper, Spencer. ''[https://www.bloomberg.com/graphics/2016-amazon-same-day/ Amazon Doesn’t Consider the Race of Its Customers. Should It?].'' Bloomberg, 2016.
* Mars, Roman. ''[https://99percentinvisible.org/episode/the-age-of-the-algorithm/ The Age of the Algorithm].'' 99% Invisible Podcast, 2017.
* [https://www.perspectiveapi.com/#/ Google's Perspective API]
 
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<br/>
<hr/>
<hr/>
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=== Week 6: November 2 ===
=== Week 6: November 2 ===
[[HCDS_(Fall_2017)/Day_6_plan|Day 6 plan]]
[[HCDS_(Fall_2017)/Day_6_plan|Day 6 plan]]
[[:File:HCDS Week 6 slides.pdf|Day 6 slides]]


;Mixed-methods research: ''Big data vs thick data; qualitative research in data science ''
;Mixed-methods research: ''Big data vs thick data; qualitative research in data science ''
Line 191: Line 213:
;Assignments due
;Assignments due
* Reading reflection
* Reading reflection
* A2: Bias in data




Line 198: Line 221:


;Readings assigned
;Readings assigned
* R. Stuart Geiger and Aaron Halfaker. 2017. ''[https://commons.wikimedia.org/wiki/File:conflict-bots-wp-cscw.pdf 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
;Homework assigned
Line 204: Line 228:


;Resources
;Resources
* Maximillian Klein. ''[http://whgi.wmflabs.org/gender-by-language.html Gender by Wikipedia Language]''. Wikidata Human Gender Indicators (WHGI), 2017.
* Benjamin Collier and Julia Bear. ''[https://static1.squarespace.com/static/521c8817e4b0dca2590b4591/t/523745abe4b05150ff027a6e/1379354027662/2012+-+Collier%2C+Bear+-+Conflict%2C+confidence%2C+or+criticism+an+empirical+examination+of+the+gender+gap+in+Wikipedia.pdf 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. ''[https://upload.wikimedia.org/wikipedia/commons/7/77/The_Heart_Work_of_Wikipedia_Gendered,_Emotional_Labor_in_the_World%27s_Largest_Online_Encyclopedia.pdf 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
* Andrea Forte, Nazanin Andalibi, and Rachel Greenstadt. ''[http://andreaforte.net/ForteCSCW17-Anonymity.pdf Privacy, Anonymity, and Perceived Risk in Open Collaboration: A Study of Tor Users and Wikipedians]''. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17). DOI: https://doi.org/10.1145/2998181.2998273


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{{:HCDS (Fall 2017)/Day 7 plan}}
{{:HCDS (Fall 2017)/Day 7 plan}}


;Readings assigned
;Readings assigned (read both, reflect on one)
* Lilly C. Irani and M. Six Silberman. 2013. ''[https://escholarship.org/content/qt10c125z3/qt10c125z3.pdf 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. ''[http://www-users.cs.umn.edu/~bhecht/publications/goldstandards_CSCW2015.pdf 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


;Homework assigned
;Homework assigned
* Reading reflection
* Reading reflection
* A4: Crowdwork self-ethnography
* A4: Crowdwork ethnography




;Resources
;Resources
*''go here''
* WeArDynamo contributors. ''[http://wiki.wearedynamo.org/index.php?title=Basics_of_how_to_be_a_good_requester How to be a good requester]'' and ''[http://wiki.wearedynamo.org/index.php?title=Guidelines_for_Academic_Requesters Guidelines for Academic Requesters]''. Wearedynamo.org
* Wang, Tricia. ''[https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7 Why Big Data Needs Thick Data]''. Ethnography Matters, 2016.
<!-- * Wanda J. Orlikowski. 1992. ''[https://dspace.mit.edu/bitstream/handle/1721.1/2412/SWP-3428-27000158-CCSTR-134.pdf%3Bjsessionid%3D89CCB8F0923C0235DB2902AA40C25E28?sequence%3D1 Learning from Notes: organizational issues in groupware implementation]''. In Proceedings of the 1992 ACM conference on Computer-supported cooperative work (CSCW '92). DOI=http://dx.doi.org/10.1145/143457.143549 -->


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<br/>
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[[HCDS_(Fall_2017)/Day_8_plan|Day 8 plan]]
[[HCDS_(Fall_2017)/Day_8_plan|Day 8 plan]]


;User experience and big data: ''prototyping and user testing; benchmarking and iterative evaluation; UI design for data science''
[[:File:HCDS Week 8 slides.pdf|Day 8 slides]]
 
;User experience and big data: ''user-centered design and evaluation of recommender systems; UI design for data science, collaborative visual analytics''




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;Readings assigned
;Readings assigned
*Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. ''[https://md.ekstrandom.net/research/pubs/listcmp/listcmp.pdf 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. ''[https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1254&context=hicss-50 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)


;Homework assigned
;Homework assigned
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;Resources
;Resources
Snyder, Jaime. ''[https://cscw2016hcds.files.wordpress.com/2015/10/snyder_hcds20162.pdf Values in the Design of Visualizations].'' 2016 CSCW workshop on Human-Centered Data Science.
* Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. ''[http://files.grouplens.org/papers/mcnee-chi06-hri.pdf 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. ''[https://crowston.syr.edu/sites/crowston.syr.edu/files/cpa137-crowstonA.pdf 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. ''[https://md.ekstrandom.net/research/pubs/behaviorism/BehaviorismIsNotEnough.pdf 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. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017.
* Xavier Amatriain and Justin Basilico. ''[https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429 Netflix Recommendations: Beyond the 5 stars].'' Netflix Tech Blog, 2012.
*Fabien Girardin. ''[https://medium.com/@girardin/experience-design-in-the-machine-learning-era-e16c87f4f2e2 Experience design in the machine learning era].'' Medium, 2016.
* Brian Whitman. ''[https://notes.variogr.am/2012/12/11/how-music-recommendation-works-and-doesnt-work/ How music recommendation works - and doesn't work].'' Variogram, 2012.
* Paul Lamere. ''[https://musicmachinery.com/2011/05/14/how-good-is-googles-instant-mix/ How good is Google's Instant Mix?].'' Music Machinery, 2011.
* Snyder, Jaime. ''[https://cscw2016hcds.files.wordpress.com/2015/10/snyder_hcds20162.pdf Values in the Design of Visualizations].'' 2016 CSCW workshop on Human-Centered Data Science.


<br/>
<br/>
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;Assignments due
;Assignments due
* Reading reflection
* Reading reflection
* A4: Crowdwork self-ethnography
* A4: Crowdwork ethnography


;Agenda
;Agenda
Line 275: Line 320:


;Readings assigned
;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. [[https://mako.cc/academic/hill_etal-cdsw_chapter-DRAFT.pdf Preprint/Draft PDF]]
* Bivens, R. and Haimson, O.L. 2016. ''[http://journals.sagepub.com/doi/pdf/10.1177/2056305116672486 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. ''[http://arischlesinger.com/wp-content/uploads/2017/03/chi2017-schlesinger-intersectionality.pdf 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
;Homework assigned
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;Resources
;Resources
* Berney, Rachel, Bernease Herman, Gundula Proksch, Hillary Dawkins, Jacob Kovacs, Yahui Ma, Jacob Rich, and Amanda Tan. ''[https://dssg.uchicago.edu/wp-content/uploads/2017/09/berney.pdf 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. ''[https://cscw2016hcds.files.wordpress.com/2015/10/dasgupta_hcds2016.pdf 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 30 ===
=== Week 10: November 30 ===
[[HCDS_(Fall_2017)/Day_10_plan|Day 10 plan]]
[[HCDS_(Fall_2017)/Day_10_plan|Day 10 plan]]
[[:File:HCDS Week 10 slides.pdf|Day 10 slides]]


;Communicating methods, results, and implications: translating for non-data scientists ''
;Communicating methods, results, and implications: translating for non-data scientists ''
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;Readings assigned
;Readings assigned
* Megan Risdal, ''[http://blog.kaggle.com/2016/06/29/communicating-data-science-a-guide-to-presenting-your-work/ Communicating data science: a guide to presenting your work].'' Kaggle blog, 2016.
* Marilynn Larkin, ''[https://www.elsevier.com/connect/how-to-give-a-dynamic-scientific-presentation How to give a dynamic scientific presentation].'' Elsevier Connect, 2015.


;Homework assigned
;Homework assigned
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;Resources
;Resources
* ''one''
* Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. ''[https://pure.tue.nl/ws/files/3484177/724656348730405.pdf 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. ''[http://files.grouplens.org/papers/p171-mcnee.pdf 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, ''[http://blog.kaggle.com/2016/08/10/communicating-data-science-why-and-some-of-the-how-to-visualize-information/ Communicating data science: Why and how to visualize information].'' Kaggle blog, 2016.
* Megan Risdal, ''[http://blog.kaggle.com/2016/06/13/communicating-data-science-an-interview-with-a-storytelling-expert-tyler-byers/ Communicating data science: an interview with a storytelling expert].'' Kaggle blog, 2016.
* Richard Garber, ''[https://joyfulpublicspeaking.blogspot.com/2010/08/power-of-brief-speeches-world-war-i-and.html Power of brief speeches: World War I and the Four Minute Men].'' Joyful Public Speaking, 2010.
* Brent Dykes, ''[https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/ Data Storytelling: The Essential Data Science Skill Everyone Needs].'' Forbes, 2016.


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[[HCDS_(Fall_2017)/Day_11_plan|Day 11 plan]]
[[HCDS_(Fall_2017)/Day_11_plan|Day 11 plan]]


;Future of human centered data science: ''case studies from research, industry, and policy; final presentations''
;Future of human centered data science: course wrap up, final presentations''




Latest revision as of 21:35, 8 December 2017


Week 1: September 28[edit]

Day 1 plan

Day 1 slides

Course overview
What is data science? What is human centered? What is human centered data science?
Assignments due
  • fill out the pre-course survey
Agenda
  • Course overview & orientation
  • What do we mean by "data science?"
  • What do we mean by "human centered?"
  • How does human centered design relate to data science?


Readings assigned
Homework assigned
  • Reading reflection
Resources



Week 2: October 5[edit]

Day 2 plan

Day 2 slides

Ethical considerations in Data Science
privacy, informed consent and user treatment


Assignments due
  • Week 1 reading reflection
Agenda
  • Informed consent in the age of Data Science
  • Privacy
    • User expectations
    • Inferred information
    • Correlation
  • Anonymisation strategies


Readings assigned
  • Read: Markham, Annette and Buchanan, Elizabeth. Ethical Decision-Making and Internet Researchers. Association for Internet Research, 2012.
  • 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



Week 3: October 12[edit]

Day 3 plan

Day 3 slides

Data provenance, preparation, and reproducibility
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 2 reading reflection
Agenda
  • Final project overview
  • Introduction to open research
  • Understanding data licensing and attribution
  • Supporting replicability and reproducibility
  • Making your research and data accessible
  • Working with Wikipedia datasets
  • Assignment 1 description


Readings assigned
Homework assigned
Examples of well-documented open research projects
Examples of not-so-well documented open research projects
Other resources




Week 4: October 19[edit]

Day 4 plan

Day 4 slides

Study design
understanding your data; framing research questions; planning your study


Assignments due
  • Reading reflection
  • A1: Data curation
Agenda
  • How Wikipedia works (and how it doesn't)
  • guest speaker: Morten Warnke-Wang, Wikimedia Foundation
  • Sources of bias in data science research
  • Sources of bias in Wikipedia data


Readings assigned


Homework assigned
  • Reading reflection
  • A2: Bias in data


Resources



Week 5: October 26[edit]

Day 5 plan

Day 5 slides

Machine learning
ethical AI, algorithmic transparency, societal implications of machine learning
Assignments due
  • Reading reflection
Agenda
  • Social implications of machine learning
  • Consequences of algorithmic bias
  • Sources of algorithmic bias
  • Addressing algorithmic bias
  • Auditing algorithms


Readings assigned
Homework assigned
  • Reading reflection
  • A3: Final project plan


Resources



Week 6: November 2[edit]

Day 6 plan

Day 6 slides

Mixed-methods research
Big data vs thick data; qualitative research in data science


Assignments due
  • Reading reflection
  • A2: Bias in data


Agenda
  • Guest speakers: Aaron Halfaker, Caroline Sinders (Wikimedia Foundation)
  • Mixed methods research
  • Ethnographic methods in data science
  • Project plan brainstorm/Q&A session


Readings assigned
Homework assigned
  • Reading reflection


Resources



Week 7: November 9[edit]

Day 7 plan

Human computation
ethics of crowdwork, crowdsourcing methodologies for analysis, design, and evaluation


Assignments due
  • Reading reflection
  • A3: Final project plan


Agenda
  • the role of qualitative research in human centered data science
  • scaling qualitative research through crowdsourcing
  • types of crowdwork
  • ethical and practical considerations for crowdwork
  • Introduction to assignment 4: Mechanical Turk ethnography


Readings assigned (read both, reflect on one)
Homework assigned
  • Reading reflection
  • A4: Crowdwork ethnography


Resources



Week 8: November 16[edit]

Day 8 plan

Day 8 slides

User experience and big data
user-centered design and evaluation of recommender systems; UI design for data science, collaborative visual analytics


Assignments due
  • Reading reflection
Agenda
  • HCD process in the design of data-driven applications
  • understanding user needs, user intent, and context of use in recommender system design
  • trust, empowerment, and seamful design
  • HCD in data analysis and visualization
  • final project lightning feedback sessions


Readings assigned
Homework assigned
  • Reading reflection


Resources



Week 9: November 23[edit]

Day 9 plan

Human-centered data science in the wild
community data science; data science for social good
Assignments due
  • Reading reflection
  • A4: Crowdwork ethnography
Agenda
  • NO CLASS - work on your own


Readings assigned
Homework assigned
  • Reading reflection
Resources



Week 10: November 30[edit]

Day 10 plan

Day 10 slides

Communicating methods, results, and implications
translating for non-data scientists


Assignments due
  • Reading reflection


Agenda
  • communicating about your research effectively and honestly to different audiences
  • publishing your research openly
  • disseminating your research
  • final project workshop


Readings assigned


Homework assigned
  • Reading reflection
  • A5: Final presentation
Resources



Week 11: December 7[edit]

Day 11 plan

Future of human centered data science
course wrap up, final presentations


Assignments due
  • Reading reflection
  • A5: Final presentation


Agenda
  • future directions of of human centered data science
  • final presentations


Readings assigned
  • none!
Homework assigned
  • none!
Resources
  • one



Week 12: Finals Week[edit]

  • NO CLASS
  • A6: FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 10
  • LATE PROJECT SUBMISSIONS NOT ACCEPTED.
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